Image processing device and image processing method for image correction

ABSTRACT

The present disclosure is to generate a high-quality image by correcting a predetermined correction target image based on a plurality of input images. In an image processing device 3, an image correcting section 160 detects a user tap gesture on a touch panel 250. When the position of the tap gesture is within foreground candidate areas detected by a foreground candidate area detecting section 140, the image correcting section 160 corrects a base image set by a base image setting section 120 in the areas corresponding to the foreground candidate areas.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. Pat. No. 9,659,350,filed on Jan. 28, 2015, which claims the priority benefit of Japanapplication serial no. 2014-017230, filed on Jan. 31, 2014. The entiretyof each of the above-mentioned patent applications is herebyincorporated by reference herein and made a part of this specification.

TECHNICAL FIELD

The present disclosure relates to an image processing device and animage processing method.

BACKGROUND ART

When a person takes a commemorative photograph at a holiday resort, forexample, moving objects such as other people or cars may enter the framein addition to the photographic subject, so it is difficult to take aphotograph in suitable conditions, i.e. a condition in which only aphotographic subject is present in the frame. There are conventionaltechniques for this problem, such as those disclosed in Patent Document1 and Patent Document 2, which solve the problem by taking an image ofan identical scene plural times, and extracting supposedly optimal partsand compositing them with each other.

CITATION LIST Patent Literature

-   Patent Document 1: US 2012/0320237A-   Patent Document 2: EP 2360644B

SUMMARY Technical Problem

When photographed images as described above are input as input images soas to generate an image in which only a photographic subject is shownfrom the input images, there is a requirement for moving objects to becorrectly detected and deleted from the image. One of the availabletechniques for this is, for example, generating a background image inwhich a photographic subject is shown in the background by calculating aweighted average of a plurality of input images.

However, a problem with this technique is the difficulty of obtaining ahigh-quality image because of small positional misalignments that occurbetween the plurality of input images due to the difficulty incompletely correcting misalignments between the plurality of inputimages caused by hand-shaking or the like, and due to blur caused by,for example, leaves of a tree shaken by the wind in the correspondingareas in the generated background image. Further, another problem isthat an area with a crowd or another constantly moving object cannot bedetermined as either a background or a foreground, so such areas with amoving object are not properly corrected and a ghost appears.

The present disclosure was made in consideration of the above-describedproblems, and is to propose a novel technique for correcting acorrection target image that is based on a plurality of input images andgenerating a high-quality image.

Solution to Problem

In order to solve the above-described problems, the following means areemployed. The reference signs used in the following description ofembodiments and drawings are added for the purpose of reference.However, the components of the present disclosure are not intended to belimited to those represented by the reference signs.

A first disclosure is an image processing device (the image processingdevice 3), including: an image correcting unit (the image correctingsection 160) configured to set a correction target area (the foregroundcandidate areas) in a correction target image (one of the input images,e.g. the base image) based on a plurality of input images (the inputphotographed images) and to correct the correction target area by usingat least one of the plurality of input images as a correction image.

A twenty-eighth disclosure is an image processing method, including:setting a correction target area (the foreground candidate areas) in acorrection target image (one of the input images, e.g. the base image)based on a plurality of input images (the input photographed images) andcorrecting the correction target area by using at least one of theplurality of input images as a correction image.

By means of the first disclosure and the like, the correction targetarea in the correction target image, which is set based on the pluralityof input images, is corrected by using at least one of the plurality ofinput images as the correction image. Therefore, a high-quality imagecan be generated.

A second disclosure may be the image processing device according to thefirst disclosure, wherein the image correcting unit sets as thecorrection image an input image that satisfies a predeterminedrelationship (similarity of image data) with a reference imageassociated with the plurality of input images (the background imageobtained by calculating the weighted average of the plurality of inputimages) with respect to image data of an area corresponding to thecorrection target area, and corrects the correction target area in thecorrection target image by using the image data of the areacorresponding to the correction target area of the correction image.

By means of the second disclosure, an input image that satisfies apredetermined relationship with the reference image associated with theplurality of input images with respect to image data of an areacorresponding to the correction target area is set as the correctionimage, and the image data of the area corresponding to the correctiontarget area of the correction image is used for image correction.Therefore, the correction target area in the correction target image canbe properly corrected. When there is a small positional misalignmentbetween the plurality of input images or if there are leaves of treesshaken by the wind in the image, a high-quality image can be generatedsince the image data in these areas is corrected by using the image datacorresponding to these areas of the correction image.

A third disclosure may be the image processing device according to thefirst or second disclosure, further including: a reliability determiningunit (the processing section 100) configured to determine a reliabilityof the correction target area being in a foreground, wherein the imagecorrecting unit makes a determination as to whether to correct thecorrection target area based on the reliability determined by thereliability determining unit.

By means of the third disclosure, it can be properly determined as towhether to correct the correction target area based on the determinedreliability of the correction target area being in the foreground.

A fourth disclosure may be the image processing device according to thethird disclosure, further including: an appearance changing areadetecting unit (processing unit 100) configured to detect an appearancechanging area where an appearance of a subject changes between theplurality of input images, wherein the reliability determining unitdetermines the reliability, in which the appearance changing areadetected by the appearance changing area detecting unit is set as thecorrection target area, and the image correcting unit makes adetermination as to whether to correct the appearance changing areabased on the reliability determined by the reliability determining unit.

The term “appearance changing area” refers to an area where a subject ismoving or an area where the appearance of the subject is changing due toa change of light during the photographing and the like. By means of thefourth disclosure, an area where the appearance of a subject changesbetween the plurality of input images is detected, and it can bedetermined as to whether to correct the detected appearance changingarea based on the reliability. Therefore, when there is an area where asubject is constantly moving, such as a crowd, or an area where theappearance of a subject changes according to light, such areas can bedetected and corrected as the appearance changing area.

A fifth disclosure may be the image processing device according to thethird or fourth disclosure, wherein the reliability determining unitdetermines the reliability with respect to each processing pixel unit(single pixel, a group of pixels) of the correction target image, andthe image correcting unit corrects the correction target area, in whichan area composed of a group of continuing processing pixel units thatsatisfy a predetermined condition of the reliability (“high”reliability) is set as the correction target area.

By means of the fifth disclosure, the reliability of each processingpixel unit of the correction target image being in the foreground can bedetermined, and the correction can be performed on the correction targetarea that is an area composed of a group of continuing processing pixelunits that satisfy a predetermined condition of the determinedreliability. As used herein, the term processing pixel unit refers to anarea composed of at least one pixel.

A sixth disclosure is the image processing device according to any oneof the third to fifth disclosures, further including: a relative valuecalculating unit (the difference value calculating section 11, thedifference value calculating section 111) configured to calculate arelative value for the respective plurality of input images with respectto each predetermined processing pixel unit (single pixel, a pluralityof pixels), the relative value representing relative relationship (thedifference value of pixel value) between the respective plurality ofinput images and the reference image (the background image obtained bycalculating the weighted average of the plurality of input images)associated with the plurality of input images; an input imagedetermining unit (the minimum difference input image determining unit17, the non-base minimum difference input image determining unit 117)configured to determine an input image that satisfies a specificcondition of the relative value (minimum difference value) calculated bythe relative value calculating unit from among the plurality of inputimages with respect to the processing pixel unit; and an imageprocessing data generating unit (the minimum difference map datagenerating section 19, the non-base minimum difference map datagenerating section 119) configured to generate an image processing data(the minimum difference map data 27, the non-base minimum difference mapdata 873) that stores identification information (the image number) foridentifying the input image determined by the input image determiningunit with respect to each processing pixel unit, wherein the imagecorrecting unit corrects the correction target image by using thecorrection image and the image processing data generated by the imageprocessing data generating unit.

A twenty-ninth disclosure may be the image processing method accordingto the twenty-eighth disclosure, further including: calculating relativevalue for the respective plurality of input images with respect to eachpredetermined processing pixel unit (single pixel, or a plurality ofpixels), the relative value representing relative relationship (thedifference value of the pixel value) between the respective plurality ofinput images and the reference image (the background image obtained bycalculating the weighted average of the plurality of input images)related to the plurality of input images; determining an input imagethat satisfies a specific condition (minimum difference value) of therelative value calculated by the relative value calculating unit fromamong the plurality of input images with respect to the processing pixelunit; and generating an image processing data (the minimum differencemap data 27, or the non-base minimum difference map data 873) thatstores identification information (the image number) for identifying theinput image determined by the input image determining unit with respectto each processing pixel unit, wherein the correcting step includescorrecting the correction target image by using the correction image andthe generated image processing data.

By means of the sixth disclosure and the like, the image processing datathat stores the identification information for identifying the inputimage that satisfies a specific condition on a processing pixel unitbasis can be generated based on the relative value that represent therelative relationship between the respective plurality of input imagesand the reference images related to the plurality of input images. Asused herein, the term relative value refers to a value that representsthe relative relationship between the input images and the referenceimage, including, for example, the difference value of the pixel valuebetween the input images and the reference image. Further, the specificcondition is a predetermined condition, which can be selected fromvarious conditions including, for example, the relative value that isthe maximum or the minimum, the relative value that is the median value,the relative value that is the average, or the like. By using the imageprocessing data thus generated and the correction image, the correctiontarget image can be properly corrected.

A seventh disclosure may be the image processing device according to thesixth disclosure, wherein the image processing data generating unitstores the relative value (the minimum difference values) that satisfiesthe specific condition in the image processing data in association withthe identification information (the image numbers) with respect to eachprocessing unit.

By means of the seventh disclosure, the relative value (the minimumdifference values) that satisfies the specific condition is stored inthe image processing data in association with the identificationinformation (the image numbers) with respect to each processing unitbasis. Therefore, it becomes possible to handle the relative value thatsatisfies the specific condition and the respective input images assingle data.

An eighth disclosure may be the image processing device according to thesixth or seventh disclosure, further including: a reducing unit (theanalysis image generating section 130) configured to reduce each of theplurality of input images, wherein the relative value calculating unitcalculates the relative value for the respective plurality of inputimages reduced by the reducing unit with respect to each processing unitpixel, the relative value representing the relative relationship betweenthe reduced respective plurality of input images and the reference imagethat has the same size as the reduced plurality of input images, and theimage processing data generating unit generates the image processingdata that has the same size as the reduced plurality of input images.

By means of the eighth disclosure, the image processing data with thesame size as the reduced plurality of input images can be generated.Since the large size of the input images causes a heavy processing loadin the following image correction, each of the plurality of input imagesis reduced. Then, the image processing data with the same size as thereduced input images is generated. In this way, the processing load ofthe image correction using the image processing data can be reduced.

A ninth disclosure may be the image processing device according to anyone of the sixth to eighth disclosures, further including: a foregroundcandidate area detecting unit (foreground candidate area detectingsection 140) configured to detect a foreground candidate area by usingthe plurality of input images and the image processing data generated bythe image processing data generating unit, the foreground candidate areabeing a candidate of an area being a foreground with respect to thereference image, wherein the reliability determining unit determines thereliability, in which the foreground candidate area detected by theforeground candidate area detecting unit is set as the correction targetarea, and the image correcting unit corrects the correction targetimage, in which the foreground candidate area detected by the foregroundcandidate area detecting unit is set as the correction target area.

By means of the ninth disclosure, the foreground candidate area, whichis a candidate of an area being a foreground with respect to thereference image, can be detected by using the plurality of input imagesand the generated image processing data. Then, the detected foregroundcandidate area is set as the correction target area, and the correctiontarget image can be corrected based on the determined reliability.

A tenth disclosure may be the image processing device according to theninth disclosure, further including: a base image setting unit (the baseimage setting section 120) configured to select a base image from amongthe plurality of input images, wherein the input image determining unitincludes an adequate non-base image determining unit (the non-baseminimum difference input image determining unit 117) configured todetermine an adequate non-base image that satisfies the specificcondition of the relative value from among non-base images with respectto each processing pixel unit, the non-base images being the pluralityof input images excluding the base image, the image processing datagenerating unit includes an adequate non-base image processing datagenerating unit (the non-base minimum difference map data generatingsection 119) configured to generate adequate non-base image processingdata that stores the identification information for identifying theadequate non-base image determined by the adequate non-base imagedetermining unit with respect to each processing pixel unit, theforeground candidate area detecting unit detects the foregroundcandidate area by using the base image and the adequate non-base imageprocessing data generated by the adequate non-base image processing datagenerating unit, and the image correcting unit corrects the correctiontarget image, in which the base image selected by the base image settingunit is set as the correction target image.

By means of the tenth disclosure, an adequate non-base image thatsatisfies the specific condition of the relative values is determinedfrom among non-base images, which are the plurality of input imagesexcluding the base image, with respect to each processing pixel unit,and the adequate non-base image processing data that stores theidentification information for identifying the determined adequatenon-base image with respect to each processing pixel unit. The baseimage may be set by the user selecting an image from among the pluralityof input images or it may be set according to a predetermined rule suchas selecting the first input image or the last input image. By using thebase image thus selected and the adequate non-base image processing datagenerated on the basis of the non-base images, the foreground candidatearea can be correctly detected. Then, by correcting the base image asthe correction target image, a high-quality image can be obtained.

An eleventh disclosure may be the image processing device according tothe ninth or tenth disclosure, wherein the image correcting unitcorrects the foreground candidate area of the correction target image,in which the input image corresponding to the identification informationstored in the image processing data is used as the correction image.

By means of the eleventh disclosure, the foreground candidate area ofthe correction target image can be properly corrected by using the inputimage corresponding to the identification information stored in theimage processing data as the correction image.

A twelfth disclosure may be the image processing device according to anyone of the ninth to eleventh disclosures, wherein the image correctingunit corrects an image data of the foreground candidate area of thecorrection target image by using an image data of an area correspondingto the foreground candidate area of the correction image.

By means of the twelfth disclosure, since an image data of theforeground candidate area of the correction target image is corrected byusing an image data of an area corresponding to the foreground candidatearea of the correction image, the foreground candidate area of thecorrection target image can be properly corrected.

A thirteenth disclosure may be the image processing device according toany one of the ninth to twelfth disclosures, wherein the imagecorrecting unit corrects the foreground candidate area of the correctiontarget image by using image correction processing (replacementprocessing or pasting processing) that is changed according to thereliability of the foreground candidate area.

By means of the thirteenth disclosure, image correction that is suitedto the reliability of the foreground candidate area can be performed byusing image correction processing (replacement processing or pastingprocessing) that is changed according to the reliability of theforeground candidate area.

A fourteenth disclosure may be the image processing device according tothe thirteenth disclosure, wherein, when the foreground candidate areasatisfies a predetermined high reliability condition (“high”reliability) of the reliability, the image correcting unit performs theimage correction processing that replaces an image data of theforeground candidate area of the correction target image with an imagedata of an area corresponding to the foreground candidate area of thecorrection image.

By means of the fourteenth disclosure, when the foreground candidatearea satisfies a predetermined high reliability condition, imagecorrection processing is performed wherein image data of the foregroundcandidate area of the correction target image is replaced with imagedata of an area corresponding to the foreground candidate area of thecorrection image. Therefore, the foreground candidate area with a highreliability of being in the foreground can be properly corrected.

A fifteenth disclosure may be the image processing device according tothe thirteenth or fourteenth disclosure, wherein, when the foregroundcandidate area satisfies a predetermined low reliability condition ofthe reliability, the image correcting unit performs the image correctionprocessing that extracts an image data that satisfies a predeterminedrelationship with image data of the foreground candidate area from thecorrection image, and pastes the extracted image data to the foregroundcandidate area of the correction target image.

By means of the fifteenth disclosure, when the foreground candidate areasatisfies a predetermined low reliability condition, image correctionprocessing is performed wherein image data is extracted that satisfies apredetermined relationship with an image data of the foregroundcandidate area from the correction image, and the extracted image datais pasted to the foreground candidate area of the correction targetimage. Therefore, the foreground candidate area with a low reliabilityof being in the foreground can be properly corrected.

A sixteenth disclosure is the image processing device according to anyone of the ninth to fifteenth disclosures, further including: a displayunit (the display section 300); and a display controlling unit (thedisplay controlling section 170) configured to control the display unitto display the reliability of the foreground candidate area.

By means of the sixteenth disclosure, the user can check the reliabilityof the foreground candidate area on the display screen.

A seventeenth disclosure may be the image processing device according tothe sixteenth disclosure, wherein the display controlling unit controlsthe display unit to display the reliability of the foreground candidateareas in a user-recognizable manner (e.g. displaying the foregroundcandidate area or the contour thereof in different colors, putting adifferent mark on the foreground candidate area according to thereliability, or displaying the foreground candidate area with differentpatterns or hatching according to the reliability).

By means of the seventeenth disclosure, the reliability of theforeground candidate areas is displayed in a user-recognizable manner.Therefore, the user can immediately find the reliability of each of theforeground candidate areas.

An eighteenth disclosure may be the image processing device according toany one of the ninth to seventeenth disclosures, further including: anobject detecting unit (the processing section 100) configured to detectan object from the correction target image; and a preferentialcorrection area determining unit (the processing section 100) configuredto determine a preferential correction area based on the reliability anda positional relationship between an area of the object detected by theobject detecting unit and the foreground candidate area, thepreferential correction area being an area in the correction targetimage that is preferentially corrected.

By means of the eighteenth disclosure, the preferential correction area,which is an area that is preferentially corrected in the correctiontarget image, can be automatically determined based on the positionalrelationship between the detected object area and the foregroundcandidate area and the determination result of the reliability of theforeground candidate area.

A nineteenth disclosure may be the image processing device according toany one of the ninth to eighteenth disclosures, wherein the reliabilitydetermining unit determines the reliability of the foreground candidatearea based on at least either one of a size of the foreground candidatearea and the relative values of pixels constituting the foregroundcandidate area stored in the image processing data.

By means of the nineteenth disclosure, the reliability of the foregroundcandidate area can be determined based on at least either a size of theforeground candidate area or the relative values of pixels constitutingthe foreground candidate area stored in the image processing data. It ishighly probable that a small foreground candidate area is composed ofpixels that accidentally exhibit a large difference between the basedifference value and the non-base minimum difference value due to noiseor the like. For this reason, it is preferred that the reliability ofsuch foreground candidate areas is determined as low. Further, althoughit depends on the specific condition, the relative values stored in theimage processing data tend to have unique characteristics in an areawhere it is difficult to properly correct the foreground area due to,for example, an object that is constantly moving. Accordingly, thedifficulty of correcting the foreground candidate area can be estimatedfrom the relative values of the pixels constituting the foregroundcandidate area, which are stored in the image processing data. If theimage correction is performed on an area with a high difficulty ofcorrection, a ghost may appear as described above. Therefore, it ispreferred that the reliability of such areas is determined as low.

A twentieth disclosure may be the image processing device according toany one of the ninth to nineteenth disclosure, further including: anoperating unit (the operating section 200, the touch panel 250); adisplay unit (the display section 300); a display controlling unit (thedisplay controlling section 170) configured to control the display unitto display a base image selected from the plurality of input images; adetecting unit (the processing section 100) configured to detect a useroperation (a tap gesture) that specifies a position on the base imagedisplayed on the display unit; and a specified position determining unit(the processing section 100) configured to determine whether theposition specified by the user operation is included in the foregroundcandidate area in response to the detection by the detecting unit,wherein, when the specified position determining unit determines thatthe specified position is included in the foreground area, the imagecorrecting unit corrects the foreground candidate area of the correctiontarget image, and the display controlling unit controls the display unitto display a corrected image corrected by the image correcting unit.

By means of the twentieth disclosure, when the user performs anoperation to specify a position in the displayed base image selectedfrom the plurality of input images, and the position specified by theuser operation is included in the foreground candidate area, the areacorresponding to the foreground candidate area can be corrected. Thatis, the area corresponding to the foreground candidate area can becorrected in response to the user operation. Then, by controlling thedisplay unit to display the corrected image, the user can check thecorrected image.

A twenty-first disclosure may be the image processing device accordingto the twentieth disclosure, further including: an image correctionnecessity data generating unit (the processing section 100) configuredto generate an image correction necessity data (the foreground candidatearea data 877) based on a determination result of the specified positiondetermining unit, the image correction necessity data being informationon whether to correct the foreground candidate area of the correctiontarget image specified by the user operation, wherein the imagecorrecting unit makes a determination as to whether to correct theforeground candidate area of the correction target image based on theimage correction necessity data generated by the image correctionnecessity data generating unit.

By means of the twenty-first disclosure, since the image correctionnecessity data according to the user operation is generated, necessityof the image correction can be determined for each foreground candidatearea. Therefore, even when a plurality of foreground candidate areas arespecified by the user, the image correction can be correctly performedon the specified foreground candidate areas.

A twenty-second disclosure may be the image processing device accordingto the eighteenth disclosure, wherein the image correcting unit correctsthe preferential correction area of the correction target image, and theimage processing device further includes: a display unit (the displaysection 300); and a display controlling unit (the display controllingsection 170) configured to control the display unit to display acorrected image corrected by the image correcting unit.

By means of the twenty-second disclosure, since the preferentialcorrection area is corrected, and the corrected image displayed, theuser can check the corrected image in which the preferential correctionarea is automatically and preferentially corrected.

A twenty-third disclosure may be the image processing device accordingto the twenty-second disclosure, wherein the display controlling unitcontrols the display unit to display the preferential correction area ina manner different from the foreground candidate area.

By means of the twenty-third disclosure, since the preferentialcorrection area is displayed in a manner different from the foregroundcandidate area, the user can immediately find the area to bepreferentially corrected.

A twenty-fourth disclosure is an image processing device, including: animage storing unit (the memory section 800) for storing a plurality ofinput images; a calculating unit (the image processing section 100)configured to calculate a replacement candidate area in a base image anda replacement reliability associated with the replacement candidate areabased on the stored plurality of input images, the base image being amost recent input image of the plurality of input images; and a displayunit (the display section 300) configured to display an image in whichthe replacement candidate area or a contour of the replacement candidatearea is superimposed on the base image, wherein, when the base image isupdated as a result of an input of a new image, the calculating unitre-calculates the replacement candidate area and the replacementreliability and the display unit refreshes the image displayed on thedisplay unit based on a result of the re-calculation.

A thirtieth disclosure is an image processing method, including: storinga plurality of input images; calculating a replacement candidate areaand a replacement reliability associated with the replacement candidatearea based on the stored plurality of input images; and displaying animage in which the replacement candidate area or a contour of thereplacement candidate area is superimposed on a base image on apredetermined display unit, the base image being a most recent inputimage of the plurality of input images, wherein the calculating includesre-calculating the replacement candidate area and the replacementreliability when the base image is updated as a result of an input of anew image, and the displaying includes refreshing the image displayed onthe display unit based on a result of the re-calculating.

By means of the twenty-fourth disclosure and the like, a plurality ofinput images is stored, and the replacement candidate area and thereplacement reliability associated with the replacement candidate areaare calculated based on the stored plurality of input images. Then, theimage is displayed in which the replacement candidate area or thecontour of the replacement candidate area is superimposed on the baseimage that is the most recent input image of the plurality of inputimages. Therefore, the user can immediately understand the replacementcandidate area. Then, when the base image is updated as a result of aninput of a new image, the replacement candidate area and the replacementreliability can be constantly refreshed in response to an input of a newimage by re-calculating the replacement candidate area and thereplacement reliability. Then, the image displayed on the display unitis refreshed based on the result of the re-calculation. Therefore, theuser can understand the latest replacement candidate area in real time.

A twenty-fifth disclosure may be the image processing device accordingto the twenty-fourth disclosure, wherein the display unit displays thereplacement candidate area or the contour of the replacement candidatearea in a manner that reflects the associated replacement reliability.

By means of the twenty-fifth disclosure, the replacement candidate areaor the contour of the replacement candidate area is displayed in amanner that reflects the associated replacement reliability. Therefore,the user can immediately understand the reliability of the replacementcandidate area.

A twenty-sixth disclosure may be the image processing device accordingto the twenty-fourth or twenty-fifth disclosure, further including: anarea specifying unit (the operating section 200, the touch panel 250)for specifying an area; and an informing unit (the display unit 300)configured to inform a user of whether the associated replacementreliability satisfies a predetermined condition when the area specifiedby the area specifying unit is included in the replacement candidatearea calculated by the calculating unit.

By means of the twenty-sixth disclosure, when the area specified by thearea specifying unit is included in the replacement candidate areacalculated by the calculating unit, if the associated replacementreliability satisfies a predetermined condition, the user is informedthat the predetermined condition is satisfied. For example, the user isinformed that the replacement reliability is equal to or greater than apredetermined level. Therefore, the user can understand whichreplacement candidate area has high reliability.

A twenty-seventh disclosure may be the image processing device accordingto any one of the twenty-fourth to twenty-sixth disclosures, furtherincluding: an area specifying unit for specifying an area (the operatingunit 200, the touch panel 250); and an image correcting unit (the imagecorrecting section 160) configured to correct an area corresponding tothe replacement candidate area when the area specified by the areaspecifying unit is included in the replacement candidate area and theassociated replacement reliability satisfies a predetermined condition,wherein the display unit displays a corrected image corrected by theimage correcting unit.

By means of the twenty-seventh disclosure, when the specified area isincluded in the replacement candidate area, and the associatedreplacement reliability satisfies a predetermined condition, the areacorresponding to the replacement candidate area is corrected. Forexample, if the replacement reliability is equal to or greater than apredetermined level, the area corresponding replacement candidate areais corrected. Therefore, the area corresponding to the replacementcandidate area with high replacement reliability can be exclusivelycorrected. Further, since the corrected image is displayed, the user cancheck the corrected image on the screen.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an image processing data generating device,illustrating an example of the functional configuration.

FIG. 2 illustrates an example of the data structure of input image data.

FIG. 3 illustrates an example of the data structure of minimumdifference map data.

FIG. 4 is a principle diagram illustrating an example of the procedureof generating minimum difference map data.

FIG. 5 is a flowchart illustrating an example of minimum difference mapdata generation processing.

FIG. 6 is a block diagram of an image processing device, illustrating anexample of its functional configuration.

FIG. 7 illustrates an example of the data structure of image data.

FIG. 8 illustrates an example of the data structure of foregroundcandidate area data.

FIG. 9 is a principle diagram illustrating an example of the procedureof generating non-base minimum difference map data.

FIG. 10 (1) is an example of a photographed image. FIG. 10 (2) is anexample of a background image. FIG. 10 (3) is an example of a backgrounddifference image.

FIG. 11 is a principle diagram for describing foreground candidate areadetection and foreground candidate area reliability determination.

FIG. 12 is a flowchart illustrating an example of image processing.

FIG. 13 is a continuation of the flowchart illustrating the imageprocessing of FIG. 12.

FIG. 14 is a flowchart illustrating an example of background imagegeneration processing.

FIG. 15 is a flowchart illustrating an example of foreground candidatearea detection processing.

FIG. 16 is a flowchart illustrating an example of foreground candidatearea reliability determination processing.

FIG. 17 is a flowchart illustrating an example of image correctionprocessing.

FIG. 18 (1) is an example of a photographed image. FIG. 18 (2) is anexample of a replacement candidate area indicating image. FIG. 18(3) isan example of a replacement candidate area overlaid image. FIG. 18 (4)is an example of a corrected image.

FIG. 19 (1) is an example of a photographed image. FIG. 19 (2) is anexample of a non-base minimum difference image. FIG. 19 (3) is anexample of a result of detecting a foreground candidate area. FIG. 19(4) is an example of a replacement candidate area indicating image.

FIG. 20 (1) is an example of a band preferential correction areapattern. FIG. 20 (2) is an example of a frame preferential correctionarea pattern. FIG. 20 (3) is an example of a triangular preferentialcorrection area pattern.

FIG. 21 illustrates an example of a recording medium.

DESCRIPTION OF EMBODIMENTS 1. First Embodiment

First, an embodiment of an image processing data generating device willbe described. The image processing data generating device generatesimage processing data that can be used for detection of a foregroundcandidate area in an image and various image processing such as imagecorrection, which will be described below in the other embodiments.

[1-1. Functional Configuration]

FIG. 1 is a block diagram of an image processing data generating device1, illustrating the functional configuration thereof.

The image processing data generating device 1 receives a plurality ofinput images and generates minimum difference map data, which is a typeof data for image processing, by using the plurality of input images anda predetermined reference image. The minimum difference map data is datain a map format that is obtained by calculating difference valuesbetween each of the plurality of input images and the reference image ona pixel basis and storing the minimum values of the calculateddifference values (hereinafter also referred to as “minimum differencevalues”) in association with the image numbers of the respective inputimages corresponding to the minimum values.

The plurality of input images that are input in the image processingdata generating device 1 may be, preferably, a plurality of photographedimages that are obtained as a result of photographing an identicalphotographic scene at plural times. Specifically, for example, the inputimages may be a plurality of frame images that are obtained by placing aphotographic subject in a predetermined photographic scene andphotographing the photographic subject at plural times at certain timeintervals while keeping the photographic subject still. The photographicsubject may be an object such as a human, an animal, an architecturalstructure and a vehicle.

The image processing data generating device 1 includes an imageprocessing data generating section 10 and a memory section 20.

The image processing data generating section 10 is a processing devicethat includes a microprocessor such as a CPU (central processing unit)and a DSP (digital signal processor) and an integral circuit such as anASIC (application specific integrated circuit).

The image processing data generating section 10 includes, as majorfunctional components, a difference value calculating section 11, aminimum difference input image determining section 17 and a minimumdifference map data generating section 19. The function of thesefunctional components will be described below.

The memory section 20 is a storage device that includes a non-volatileor volatile memory such as a ROM (read only memory), a flash memory anda RAM (random access memory), or an external storage device such as ahard disk drive, and the like.

The memory section 20 stores a minimum difference map data generatingprogram 21 that is read out and executed as minimum difference map datageneration processing (see FIG. 5) by an image processing datagenerating section 10. Further, the memory section 20 stores input imagedata 23, reference image data 25 and minimum difference map data 27.

The input image data 23 is the digital data of the plurality of inputimages that are input in the image processing data generating device 1.FIG. 2 illustrates an example of the data structure thereof.

For each input image, an image number 231, which is identificationinformation for uniquely identifying an input image, and pixel valuedata 233 composed of pixel values of the pixels of the input image arestored in association with each other in the input image data 23. Inthis embodiment, the identification information for identifying an inputimage is an image number (index).

The reference image data 25 is the digital data of the reference image,which the image processing data generating section 10 references whengenerating the minimum difference map data 27 from the plurality ofinput images. Pixel value data composed of pixel values of the pixels ofthe reference image is stored in the reference image data 25.

The reference image may be any image that is related to the inputimages. For example, the reference image may be an image that isobtained by placing a photographic subject in the identical photographicscene as the input images and photographing it such that no otherunwanted object is shown in the image. As illustrated by the dashed linein FIG. 1, the reference image may be input to the image processing datagenerating device 1 either at the same time as the input images or at adifferent time separately from the input images. Further, as illustratedby the dashed arrow in FIG. 1, the reference image may be selected fromthe plurality of input images.

The minimum difference map data 27 is the data of the minimum differencemap generated by the image processing data generating section 10. FIG. 3illustrates an example of the data structure thereof.

Pixels 271 constituting each input image, minimum difference values 273of the pixels 271 and image numbers 275 corresponding to the respectiveminimum difference values 273 are stored in the minimum difference mapdata 27 in association with each other.

[1-2. Principle]

FIG. 4 is a principle diagram illustrating the procedure of generatingthe minimum difference map data. Images and data are illustrated in thediagram by solid lines, while functional blocks that perform processingare illustrated by bold solid lines, so that they are distinguishablefrom each other. The other principle diagrams are also illustrated inthe same manner.

First, with respect to each of the plurality of input images, thedifference value calculating section 11 calculates difference values ofthe pixel values on a processing pixel unit basis. The difference valuesare relative values that represent the relative relationship betweeneach input image and the reference image. In this embodiment, aprocessing pixel unit corresponds to one pixel, and the differencevalues are calculated individually for all pixels of each input image.That is, the difference values between each input image and thereference image are calculated on a pixel basis.

Then, the minimum difference input image determining section 17determines an input image that exhibits the minimum difference valueamong the difference values that are calculated on a pixel basis for theplurality of input images (hereinafter also referred to as a “minimumdifference input image”) as an input image that satisfies a specificcondition. In this embodiment, the specific condition is a conditionthat the input image is with the lowest difference value. Specifically,with respect to each pixel, the minimum difference input imagedetermining section 17 specifies the minimum value from among thedifference values calculated for the respective plurality of inputimages, which is determined as the minimum difference value. The minimumdifference input image determining section 17 then determines the inputimage that exhibits the minimum difference value as the minimumdifference input image.

Then, the minimum difference map data generating section 19 generatesthe minimum difference map data 27 of FIG. 3, in which the minimumdifference value 273 determined by the minimum difference input imagedetermining section 17 and the image number 275 of the minimumdifference input image are stored in association with each other withrespect to each of the pixels 271.

[1-3. Processing Flow]

FIG. 5 is a flowchart illustrating minimum difference map datageneration processing that is performed by the image processing datagenerating section 10 according to the minimum difference map datagenerating program 21 stored in the memory section 20.

First, the image processing data generating section 10 performs theprocessing of Loop A with respect to each combination of one of theinput images with the reference image (Step A1 to Step A11).

In the processing of Loop A, the difference value calculating section 11performs difference value calculation processing to calculate thedifference values between the pixel values of the input images and thepixel values of the reference image. The difference value calculatingsection 11 performs the processing on the in-process combination fromamong the combinations of the input images and the reference image on apixel unit basis (Step A5). Then, the image processing data generatingsection 10 repeats the processing on the next combination of an inputimage and the reference image. When the processing is performed on allcombinations of the input images and the reference image, the imageprocessing data generating section 10 terminates the processing of LoopA (Step A11).

Thereafter, the image processing data generating section 10 performs theprocessing of Loop C with respect to each pixel (Step A13 to Step A21).In the processing of Loop C, the minimum difference input imagedetermining section 17 determines the minimum difference value of thein-process pixel (Step A15). Further, the minimum difference input imagedetermining section 17 determines the minimum difference input image(Step A17).

Then, the minimum difference map data generating section 19 stores thein-process pixel 271, the minimum difference value 273 determined inStep A15 and the image number 275 of the minimum difference input imagedetermined in Step A17 in association with each other in the memorysection 20 as the minimum difference map data 27 (Step A19).

The minimum difference map data 27 is completed when the processing ofStep A15 to A19 is performed on all of the pixels. Then, the imageprocessing data generating section 10 terminates the processing of LoopC (Step A21) and terminates the minimum difference map data generationprocessing.

[1-4. Functions and Effects]

In the image processing data generating device 1, the difference valuecalculating section 11 calculates the difference values of the pixelvalues on a pixel basis with respect to each of the plurality of inputimages as the relative values that represent relative relationshipsbetween each of the input images and the reference image. Then, theminimum difference input image determining section 17 determines, withrespect to each pixel, that the input image that exhibits the minimumvalue among the difference values calculated by the difference valuecalculating section 11 is the input image that satisfies the specificcondition. Then, the minimum difference map data generating section 19generates the minimum difference map data 27 in which the minimumdifference values and the image numbers of the respective minimumdifference input images are stored in association with each other. Theminimum difference map data 27 thus generated can be used for variousimage processing.

2. Second Embodiment

Next, an embodiment of the image processing device 3 will be described.The image processing device 3, which includes the image processing datagenerating device 1 of the first embodiment, receives as input images aplurality of photographed images (frame images) that depict aphotographic scene where a photographic subject is mixed with otherunwanted objects. By using the plurality of input images, the imageprocessing device 3 then generates and outputs an image in which theunwanted objects are removed. The image processing device 3 isapplicable to a variety of electronic devices having a photographingfunction such as smartphones, tablet computers, digital cameras, PDAs(Personal Digital Assistants) and personal computers.

[2-1. Functional Configuration]

FIG. 6 is a block diagram of the image processing device 3, illustratingan example of the functional configuration thereof.

The image processing device 3 includes a processing section 100, anoperating section 200, a display section 300, a communicating section400, an imaging section 500, a clock section 600 and a memory section800.

The processing section 100 is a processing device that integrallycontrols the components of the image processing device 3 and performsvarious processing including image processing according to a variety ofprograms stored in the memory section 800 such as a system program. Theprocessing section 100 includes a processor such as a CPU and a DSP andan integrated circuit such as an ASIC.

The processing section 100 includes, as major functional components, animage processing data generating section 110, a base image settingsection 120, an analysis image generating section 130, a foregroundcandidate area detecting section 140, a foreground candidate areareliability determining section 150, an image correcting section 160 anda display controlling section 170. The function of these functionalcomponents will be described below.

The image processing data generating section 110 includes, as majorfunctional components, a difference value calculating section 111, animage-composition ratio setting section 112, a background imagegenerating section 113, a background difference value calculatingsection 114, a non-base minimum difference input image determiningsection 117 and a non-base minimum difference map data generatingsection 119. The function of these functional components will bedescribed below.

The operating section 200 includes an input device for various userinputs to the image processing device 3, such as an operation button, anoperation switch and a mouse. The operating section 200 further includesa touch panel 250 that is integrally formed with a display section 300.The touch panel 250 serves as an input interface between a user and theimage processing device 3. The operating section 200 outputs anoperation signal to the processing section 100 according to a useroperation.

The display section 300 is a display device that includes an LCD (liquidcrystal display) and the like. The display section 300 displays variousinformation based on a display signal output from the processing section100. The display section 300 is integrally formed with the touch panel250 so that they constitute a touch screen. The display section 300displays a variety of images such as photographed images and correctedimages.

The communicating section 400 is a communicating device for transmittingand receiving information to be used in the device to and from anexternal information processing device. The communicating section 400may use any of a variety of communication methods including wiredconnection via a cable compatible with a predetermined communicationstandard, connection via an intermediate device known as a cradle, whichalso serves as a charger, a wireless connection using a near fieldcommunication technique, and the like.

The imaging section 500, which is an imaging device capable of taking animage of an arbitrary scene, includes an imaging element such as a CCD(charge coupled device) image sensor and a CMOS (complementary MOS)image sensor. The imaging section 500 coverts a light signal to anelectric signal so as to output a digital data of a photographed imageto the processing section 100.

The clock section 600, which is an internal clock of the imageprocessing device 3, includes, for example, a quartz oscillator composedof a quartz resonator and an oscillating circuit. A time acquired by theclock section 600 is constantly output to the processing unit 100.

The memory section 800 is a storage device that includes a volatile ornon-volatile memory such as a ROM an EEPROM, a flash memory and an RAM,or a hard disk drive or the like. The memory section 800 stores a systemprogram for the processing section 100 to control the image processingdevice 3, and programs and data for performing a variety of imageprocessing.

In this embodiment, the memory section 800 stores an image processingprogram 810 that is read out and executed by the processing section 100as the image processing (see FIG. 12 and FIG. 13). The image processingprogram 810 includes, as sub-routines, a minimum difference map datagenerating program 21 executed as minimum difference map data generationprocessing (see FIG. 5), a background image generating program 820executed as background image generation processing (see FIG. 14), aforeground candidate area detecting program 830 executed as foregroundcandidate area detection processing (see FIG. 15), a foregroundcandidate area reliability determining program 840 executed asforeground candidate area reliability determination processing (see FIG.16) and an image correcting program 850 executed as image correctionprocessing (see FIG. 17). These processing will be described in detailwith flowcharts below.

Further, the memory section 800 stores a plurality of image data 870 andalso includes an output buffer 880 that serves as a buffer foroutputting an image.

FIG. 7 illustrates an example of the data structure of image data 870.

A plurality of photographed image data 871, non-base minimum differencemap data 873, background image data 875, foreground candidate area data877 and corrected image data 879 are stored in each of the image data870.

Each of the photographed image data 871 is digital data of aphotographed image taken by the imaging section 500, in which aphotographed date/time 871 a, an image number 871 b for uniquelyidentifying the photographed image (input image) and pixel value data871 c composed of pixel values of the pixels of the photographed imageare stored in association with each other.

The photographed date/time 871 a is stored in the photographed imagedata 871 by the processing section 100 based on a time obtained by theclock section 600. The image number 871 b is assigned by the processingsection 100 according to a predetermined rule, and is stored in thephotographed image data 871. For example, the image numbers 871 b areassigned to the photographed images in consecutive order starting from“1” by the imaging section 500 based on the respective photographingdate/time 871 a such that a smaller number is assigned to an image withan earlier photographed time. Alternatively, the image numbers 871 b maybe assigned to the photographed images in consecutive order startingfrom “1” so that a smaller number is assigned to an image with a laterphotographed time. If the image name is automatically assigned (if theimage file name is automatically assigned), the image numbers 871 b maybe assigned in the order of the image names (Image-1, Image-2, Image-3 .. . ) of the photographed images.

The non-base minimum difference map data 873 is a data for imageprocessing that is generated by the non-base minimum difference map datagenerating section 119. The non-base minimum difference map data 873will be described in detail below.

The background image data 875 is data in which pixel values of thepixels of a background image are stored. In this embodiment, thebackground image is ideally an image in which the photographic subjectis shown in the foreground while no other unwanted object is shown inthe foreground. Since a variety of unwanted objects can be present in aphotographic scene, it is difficult to remove all of such unwantedobjects. To cope with the problem, in this embodiment, an image in whichall moving objects other than the photographic subject are removed isgenerated by detecting the areas of the moving objects as foregroundcandidate areas and correcting the areas of a photographed imagecorresponding to the foreground candidate areas. The background imagecan also be obtained, for example, by placing a photographic subject ina photographic scene and photographing the scene such that no othermoving object is shown in the image. However, in this embodiment, thebackground image is automatically generated from the plurality ofphotographed images that are input from the imaging section 500.

The foreground candidate area data 877 is data on the foregroundcandidate areas. FIG. 8 illustrates an example of the data structurethereof. In this embodiment, the area in an image that is occupied by amoving object other than the photographic subject is defined as aforeground candidate area.

In the foreground candidate area data 877, labels 877 a asidentification information for uniquely identifying detected foregroundcandidate areas, constituting pixels 877 b that are the pixelsconstituting the foreground candidate areas, reliabilities 877 c ofbeing a foreground candidate of the foreground candidate areas andcorrection flags 877 d indicating whether the foreground candidate areasare to be corrected or not are stored in association with each other.The correction flags 877 d are data pertaining to image correctionnecessity that store information on whether the image correction isrequired in the foreground candidate areas. The method for detecting theforeground candidate areas and the method for determining thereliability of the foreground candidate areas will be described indetail below.

In this regard, contour calculation processing may be performed tocalculate the contours of the detected foreground candidate areas, andthe contours calculated in the contour calculation processing may bestored in the foreground candidate area data 877 in association with thecorresponding labels 877 a of the respective foreground candidate areas.Specifically, for example, pixels on the edge of the foregroundcandidate area are determined as pixels of the contours. Then, a set ofdata composed of the pixels of the contours is stored in the foregroundcandidate area 877 as a contour data.

Calculating the contours of the foreground candidate areas and storingthe contour data in the foreground candidate area data 877 may beperformed, for example, at the same time as detecting the foregroundcandidate areas or displaying the contours of the foreground candidateareas on the display section 300. The contours can be calculated basedon the data on the foreground candidate areas at any time after theforeground candidate areas are detected.

The corrected image data 879 is composed of pixel values of the pixelsof a corrected image that is obtained by the image correcting section160 performing image correction processing on a predetermined correctiontarget image. In this embodiment, a base image set by the base imagesetting section 120 is used as the correction target image to which theimage correction processing is to be performed.

The output buffer 880 is a buffer for temporarily storing the pixelvalues of the correction target image that is used in the imagecorrection processing by the image correcting section 160. The image isprocessed in the output buffer 880, and the corrected image is displayedon the display section 300 by the control of the display controllingsection 170.

[2-2. Principle]

FIG. 9 is a principle diagram illustrating the procedure of the imageprocessing data generating section 110 generating the non-base minimumdifference map data 873 in the image processing device 3.

First, the background image generating section 113 generates thebackground image by calculating a weighted average of the input imagesby using image-composition ratios (weights) that are set on a pixelbasis. The image-composition ratios can be set based on the similaritybetween the input images. Specifically, the similarity of the inputimages is determined based on a predetermined characteristic (e.g.edge), and a higher composition ratio is assigned to an input image withhigher similarity. Then, by using the set weights, the weighted averageof the input images is calculated. As a result, an area of similarity inthe input images (i.e. a background area) has a high image-compositionratio, while an area of difference in the input images (i.e. theforeground area) has a low image-composition ratio. Therefore, thebackground area is emphasized in the resultant image.

The background image thus generated is theoretically an image in whichthe photographic subject is shown but the other unwanted objects are notshown in the foreground. Therefore, the background image may also beused as the final output image in another embodiment.

However, when a user takes the images while holding the device inhis/her hand, i.e. the device is not in a stable state, such as a fixedstate, the generated background image is more likely to be blurredbecause the photographed images are easily blurred due to camera shakeand the like. Further, when an object other than the photographicsubject is moving in the photographic scene, the area of the movingobject may not be corrected sufficiently, which may result in theappearance of a ghost. The reliability of such areas being in theforeground is low. To avoid this problem, in this embodiment, thebackground image is not used as the final output image, but thecorrected image obtained by the image correction processing is used asthe final output image.

Thereafter, the background difference value calculating section 114calculates background difference values between the input images and thebackground image on a pixel basis as relative values representing therelative relationship between each of the plurality of input images andthe background image generated by the background image generatingsection 113.

Thereafter, the base image setting section 120 selects an image to beused as a reference (base image) from among the plurality ofphotographed images. To select the base image, for example, theplurality of photographed images may be displayed on the display section300, and the user may be prompted to select one of the photographedimages. Then, the selected photographed image may be set as the baseimage. Alternatively, the base image may be selected according to apredetermined rule. For example, the first or last input photographedimage may be set as the base image.

Alternatively, the base image may be set by performing characteristicvalue detection processing to detect characteristic values from therespective plurality of photographed images and selecting the base imagebased on the detected characteristic values. In this embodiment, thecharacteristic value may be, for example, edges of an image.Specifically, edge detection processing may be performed on each of theplurality of photographed images, and for example, a photographed imagecontaining the largest number of edges may be selected as the baseimage.

In this embodiment, the plurality of photographed images excluding thebase image are referred to as “non-base images”. The images obtained bycalculating the difference values between the non-base images and thebackground image are referred to as “non-base-background differenceimages”, and an image obtained by calculating the difference valuesbetween the base image and the background image is referred to as a“base-background difference image”.

FIG. 10 (1) to FIG. 10 (3) illustrate an example of the backgroundimage, the photographed image and the background difference image.

FIG. 10 (1) illustrates one of the plurality of photographed images. Inthe photographed image, a male passerby is shown as well as a woman inthe center of the photographic subject.

FIG. 10 (2) illustrates an example of the background image that isgenerated from the plurality of photographed images. In the backgroundimage, the woman of the photographic subject is shown in the center, butthe male passerby that is shown in the photographed images of FIG. 10(1) is not present. This is because the woman of the photographicsubject is extracted as the background in the above-described generationof the background image.

FIG. 10 (3) illustrates the background difference image that is obtainedby calculating the background difference between the background imageand the photographed image. In the figure, the background differenceimage is shown as a gray scale image in which a higher gray levelrepresents a higher background difference value. In the backgrounddifference image, the gray level is high (the color is white) in thepart corresponding to the male passerby as the foreground. That is, bycalculating the background difference between the background image andthe photographed image, the part corresponding to a moving object otherthan the photographic subject is extracted as the foreground.

Back to FIG. 9, based on the pixel values of the non-base-backgrounddifference images, the non-base minimum difference input imagedetermining section 117 determines the non-base minimum differencevalues and the input images corresponding to the respective non-baseminimum difference values (hereinafter referred to as the “non-baseminimum difference input image”) on a pixel basis. Then, based on thedetermination results of the non-base minimum difference input imagedetermining section 117, the non-base minimum difference map datagenerating section 119 generates the non-base minimum difference mapdata 873 in which pixels, the non-base minimum difference values of therespective pixels and the non-base minimum difference input images ofthe respective pixels are stored in association with each other.

FIG. 11 is a diagram illustrating the principle of the foregroundcandidate area detection and the procedure of the foreground candidatearea reliability determination.

The foreground candidate area detecting section 140 detects theforeground candidate areas by comparing the non-base minimum differencevalues of the non-base minimum difference map data 873 with the basedifference values of the base difference images on a pixel basis.

Specifically, with respect to each pixel, the absolute value of thedifference between the base difference value and the non-base differencevalue is calculated, and a determination is made as to whether theabsolute difference value is greater than a threshold. If the absolutevalue is greater than the threshold, the pixel is determined as a pixelthat constitutes a foreground candidate area (hereinafter referred to asa “foreground candidate area constituting pixel”). Then, a continuousarea composed of a group of such foreground candidate area pixels isdetermined as the foreground candidate area, and a label is assigned toeach of the foreground candidate areas. As used herein, the term“continuous area” means an area that is composed of one or more adjacentforeground candidate area constituting pixels.

The base difference image refers to an image that is obtained bycalculating the difference between the base image and the backgroundimage. Since the photographic subject is detected as the background inthe background image, it is theoretically supposed not to include anyother unwanted moving object. In contrast, since a moving object otherthan the photographic subject can be included in the base image, thecalculated base difference values reflect the presence of such movingobjects other than the photographic subject. Accordingly, if a movingobject other than the photographic subject is present in the base image,the background difference values becomes high in the area of the movingobject.

The non-base minimum difference value refers to the minimum differencevalue among the difference values between the non-base images and thebackground image. If a moving object other than the photographic subjectis shown in a non-base image, the moving object is supposed to be shownat a different position or to be out of the scene in another non-baseimage unless it stays at the same position. Accordingly, it is highlyprobable that the difference values of a non-base image that does notinclude any moving object other than the photographic subject isselected as the non-base minimum difference values.

Next, the foreground candidate area reliability determining section 150determines the reliability of the foreground candidate areas detected bythe foreground candidate area detecting section 140 being in theforeground. Specifically, for example, the size of each foregroundcandidate area is calculated as an index of the reliability of theforeground candidate area, and a determination is made as to whether thesize is greater than a threshold. Specifically, a determination is madeas to whether the calculated size is greater than a first threshold. Ifthe size is greater than the first threshold, the reliability of theforeground candidate area is determined as high. It is highly probablethat a small foreground candidate area is composed of pixels thataccidentally exhibit a large difference between the base differencevalue and the non-base minimum difference value due to noise or thelike. For this reason, such foreground candidate areas are determined asunreliable.

Alternatively, for example, the non-base minimum difference values ofthe pixels of each foreground candidate area may be used as anotherindex of the reliability, and a determination is made as to whether thenon-base minimum difference value is less than a threshold.Specifically, for example, a determination is made as to whether thenon-base minimum difference value in the foreground candidate area isless than a second threshold. If it is less than the second threshold,the reliability of the foreground candidate area is determined as high.The non-base minimum difference values tend to be high in an area wherean object is constantly moving. If the image correction is performed onsuch areas, a ghost may appear as described in the background section.To avoid this, the reliability of the foreground candidate areas beingin the foreground is determined based on the non-base minimum differencevalues.

As described below in a practical embodiment with a flowchart, graduatedthresholds may be set for the non-base minimum difference values, and adetermination is made using the graduated thresholds so that thereliability is determined more precisely.

By showing the user the foreground candidate areas that areautomatically detected as described above, the user can ascertain whichpart of the base image is removable as the foreground. Accordingly, theuser can designate the correction target as necessary.

[2-3. Flow of Processing]

FIG. 12 and FIG. 13 are flowcharts of the image processing that isperformed by the processing section 100 according to the imageprocessing program 810 stored in the memory section 800.

First, the processing section 100 makes a determination as to which modethe user has selected through the operating section 200 (Step B1). Ifthe selected mode is a “photographing mode” (Step B1, photographingmode), the processing section 100 controls the imaging section 500 totake a plurality of images of the same scene in response to a usershutter operation (Step B3).

Thereafter, the analysis image generating section 130 performs analysisimage generation processing (Step B5). Then, the base image settingsection 120 performs base image setting processing (Step B7). The methodof setting the base image in this step is described above.

When the user takes the images while holding the image processing device3 in his/her hand, i.e. the device is not in a stable state, such as afixed state, it is highly probable that positional misalignment occursbetween the photographed images. In consideration of a possiblemisalignment between the photographed images due to a shaking hand orthe like, alignment processing may be performed on the non-base imageswith respect to the base image. A variety of techniques can be used forthe alignment processing. For example, an alignment technique known inthe art using an optical flow can be used. Such techniques include ablock matching method, a gradient method and the like known in the art.

Further, to obtain images for analysis, reduction processing accordingto a predetermined reduction method may be performed to reduce the baseimage and the non-base images. The reason for reducing the input imagesto generate the analysis images is that the image size of thephotographed images is so large that using the original image sizecauses a heavy processing load. The above-described alignment processingand reduction processing are performed on the photographed images toobtain the analysis images. The photographed images can be reduced by amethod known in the art such as down sampling.

Then, the background image generating section 113 performs thebackground image generation processing according to the background imagegenerating program 820 stored in the memory section 800 (Step B9).

FIG. 14 is a flowchart illustrating the background image generationprocessing.

The background image generating section 113 performs the processing ofLoop D on each combination of two different analysis images (Step C1 toStep C11).

In the processing of Loop D, the difference calculating section 111performs difference value calculating processing to calculate thedifference value of each of the pixels that constitutes the in-processcombination of analysis images (Step C5). Then, the image-compositionratio setting section 112 performs image-composition ratio settingprocessing to set the image-composition ratio of the analysis images ofthe in-process combination (Step C7). When the above-describedprocessing is performed on all of the combinations of the analysisimages, the background image generating section 113 terminates theprocessing of Loop D (Step C11).

Thereafter, the background image generating section 113 generates thebackground image by compositing the analysis images with each otherusing the image-composition ratios that are set on a pixel basis (StepC13). Then, the background image generating section 113 terminates thebackground image generation processing.

Back to the image processing, after the background image generationprocessing is performed, the image processing data generating section110 sets the analysis images and the background image as the inputimages and the reference image respectively (Step B11) and performs theminimum difference map data generation processing according to theminimum difference map data generating program 21 stored in the memorysection 800 (Step B15). In the minimum difference map data generationprocessing, the non-base minimum difference map data 873 is generated bya processing similar to the processing of FIG. 5. As described above,since the analysis images are reduced images, the minimum difference mapdata 873 is also a reduced map data with the same size as the analysisimages.

Thereafter, the foreground candidate area detecting section 140 performsthe foreground candidate area detection processing according to theforeground candidate area detecting program 830 stored in the memorysection 800 (Step B17).

FIG. 15 is a flowchart of the foreground candidate area detectionprocessing.

The foreground candidate area detecting section 140 performs theprocessing of Loop F on each pixel (Step D1 to D9). In the processing ofLoop F, the foreground candidate area detecting section 140 calculatesthe difference between the base difference value of the in-process pixeland the non-base minimum difference value of the in-process pixel storedin the non-base minimum difference map data 873 (Step D3).

Then, the foreground candidate area detecting section 140 makes adetermination as to whether the difference calculated in Step D3 isgreater than a predetermined threshold (step D5). If the difference isgreater than the threshold (Step D5, Yes), it sets a foregroundcandidate flag of the in-process pixel to “ON” (Step D7). Then, theforeground candidate area detecting section 140 repeats the processingon the next pixel. When the above-described processing is performed onall of the pixels, the foreground candidate area detecting section 140terminates the processing of Loop F (Step D9).

In Step D3, the ratio of the base difference value and the non-baseminimum difference value may be calculated instead of the differencebetween the base difference value and the non-base minimum differencevalue. In this case, the determination in Step D5 is made based on theratio of the base difference value and the non-base minimum differencevalue.

Thereafter, the foreground candidate area detecting section 140 detectsthe foreground candidate areas based on the foreground candidate flag ofeach pixel. Specifically, it detects an area composed of pixels whoseforeground candidate flag is “ON” as the foreground candidate area.Then, the foreground candidate area detecting section 140 assigns thelabels 877 a to the detected foreground candidate areas and stores themin the foreground candidate area data 877 and also stores the pixelsconstituting the foreground candidate areas as the constituting pixels877 b in association with the labels 877 a (Step D15). Thereafter, theforeground candidate area detecting section 140 terminates theforeground candidate area detection processing.

Since the non-base minimum difference map data 873 is a reduced map datahaving the same size as the analysis images, the foreground candidatearea data 877 is composed of the foreground candidate areas detectedfrom the reduced image.

Back to the image processing, after the foreground candidate areadetection processing is performed, the foreground candidate areareliability determining section 150 performs the foreground candidatearea reliability determination processing according to the foregroundcandidate area reliability determining program 840 stored in the memorysection 800 (Step B19).

FIG. 16 is a flowchart illustrating the foreground candidate areareliability determination processing.

The foreground candidate area reliability determining section 150performs the processing of Loop G on each of the foreground candidateareas to which the labels 877 a are assigned in the foreground candidatearea data 877 (Step E1 to Step E21). In the processing of Loop G, theforeground candidate area reliability determining section 150 calculatesthe size of the in-process foreground candidate area (Step E3).

Then, the foreground candidate area reliability determining section 150makes a determination as to whether the size calculated in Step E3 isgreater than the first threshold (Step E7). If the condition issatisfied (Step E7, Yes), the foreground candidate area reliabilitydetermining section 150 makes a determination as to whether the non-baseminimum difference values in the in-process foreground candidate areaare less than the second threshold (Step E9).

If the condition is satisfied in Step E9 (Step E9, Yes), the foregroundcandidate area reliability determining section 150 determines thereliability of the in-process foreground candidate area as “high”(replaceable) and stores it as the reliability 877 c of the foregroundcandidate area data 877 (Step E11).

If the condition is not satisfied in Step E9 (Step E9, No), theforeground candidate area reliability determining section 150 makes adetermination as to whether the non-base minimum difference values inthe in-process foreground candidate area are less than a third thresholdthat is greater than the second threshold (Step E13). If the conditionis satisfied (Step E13, Yes), it determines the reliability of theforeground candidate area as “moderate” (replaceable with caution) andstores it as the reliability 877 c of the foreground candidate area data877 (Step E15).

If the condition is not satisfied in Step E13 (Step E13, No), theforeground candidate area reliability determining section 150 determinesthe reliability of the in-process foreground candidate area as “low”(irreplaceable) and stores it as the reliability 877 c of the foregroundcandidate area data 877 (Step E17). Then, the foreground candidate areareliability determining section 150 repeats the processing on the nextforeground candidate area.

If the condition is not satisfied in Step E7 (Step E7, No), theforeground candidate area reliability determining section 150 excludesthe in-process foreground candidate area from the processing target anddeletes the in-process foreground candidate area from the foregroundcandidate area data 877 (Step E19). Then, the foreground candidate areareliability determining section 150 repeats the processing on the nextforeground candidate area.

After Step E11 and Step E15, the foreground candidate area reliabilitydetermining section 150 determines the in-process foreground candidatearea as a candidate area for replacement (hereinafter referred to as a“replacement candidate area”). That is, it determines the foregroundcandidate areas with a reliability of “high” (replaceable) or “moderate”(replaceable with caution) as replacement candidate areas. Then, theforeground candidate area reliability determining section 150 repeatsthe processing on the next foreground candidate area.

The replacement candidate area refers to a candidate of an area wherethe pixel values are replaced with the pixel values of a differentphotographed image. It can be also referred to as a replaceable area.Further, in terms of replacing the pixel values of the area with thepixel values of a different photographed image, it can be also referredto as a substitution candidate area or a substitutable area.

In this embodiment, the foreground candidate areas with the reliabilitydetermined as “high” or “moderate” are set as the replacement candidateareas. However, the present disclosure is not limited thereto. Forexample, the foreground candidate areas with the reliability determinedas “low” may also be included in the replacement candidate areas. Inthis case, while the foreground candidate areas with “low” reliabilityare included as replacement candidates, they may be treated as areasthat are not recommended for replacement (replacement inadvisable), i.e.an area at high risk of replacement failure.

When the processing of Step E3 to Step E19 is performed on all of theforeground candidate areas, the foreground candidate area reliabilitydetermining section 150 terminates the processing of Loop G (Step E21)and terminates the foreground candidate area reliability determinationprocessing.

Back to the image processing, after the foreground candidate areareliability determination processing is performed, the displaycontrolling section 170 controls the display section 300 to superimposea mask representing the reliability of the replacement candidate areason the base image with the original size, so as to overlay the mask(Step B23).

Specifically, among the foreground candidate areas stored in theforeground candidate area data 877, the foreground candidate areas with“high” or “moderate” reliability are set as the replacement candidateareas. The set replacement candidate areas are enlarged to the originalsize based on the reduction ratio of the analysis images to thephotographed images, and the enlarged images are displayed at thecorresponding positions on the base images. In this regard, thereplacement candidate areas overlaid on the base image are painted withdifferent colors according to the reliability. For example, thereplacement candidate area with “high” reliability may be displayed in“green” as being replaceable, and the replacement candidate area with“moderate” reliability may be displayed in “yellow” as being replaceablewith caution. This allows intuitive and recognizable indication of thereliability.

As described above, the foreground candidate area with “low” reliabilitymay also be included in the replacement candidate areas. In this case,the replacement candidate area with “low” reliability may be displayedin “red” as being replacement inadvisable.

Instead of superimposing the replacement candidate areas painted withdifferent colors according to the reliability, for example, the contoursof the replacement candidate areas in different colors may besuperimposed on the base image. For example, the replacement candidatearea with “high” reliability may be displayed as a green contour, thereplacement candidate area with “moderate” reliability may be displayedas a yellow contour, and the replacement candidate area with “low”reliability may be displayed as a red contour.

Then, the processing section 100 makes a determination as to whether tochange the base image (Step B25). If it changes the base image (StepB25, Yes), it returns the processing to Step B7. If the processingsection 100 does not change the base image (Step B25, No), it makes adetermination as to whether a user tap operation on the touch panel 250is detected (Step B29). If a tap operation is not detected (Step B29,No), it proceeds the processing to Step B41.

If a tap operation is detected (Step B29, Yes), the processing section100 makes a determination as to whether the tap position is in any ofthe replacement candidate areas (Step B31). If the tap position is inthe replacement candidate areas (Step B31, Yes), the processing section100 sets the correction flag 877 d of the foreground candidate area data877 of the corresponding replacement candidate area to “ON” (Step B33).

Thereafter, the processing section 100 makes a determination as towhether the user performs a correction execution operation through theoperating section 200 (Step B35). If the correction execution operationis not performed (Step B35, No), it proceeds the processing to Step B41.

If the correction execution operation is performed (Step B35, Yes), theimage correcting section 160 performs the image correction processingaccording to the image correcting program 850 stored in the memorysection 800 (Step B37).

FIG. 17 is a flowchart illustrating the image correction processing.

First, the image correcting section 160 copies the base image into theoutput buffer 880 (Step F1). Then, the image correcting section 160sequentially performs motion correction (motion compensation) on thenon-base images (Step F3). In this embodiment, the motion correction isperformed by detecting the movement between the in-process non-baseimage and the previous non-base image (partial movement in the image,pan, tilt and the like) and correcting the previous non-base imageaccording to the detected motion amount. The motion amount can bedetected, for example, by calculating the motion vector in the image.

Thereafter, the image correcting section 160 references the foregroundcandidate area data 877 and sets the replacement candidate areas whosecorrection flag 877 d is “ON” as correction target candidate areas (StepF5). Then, the image correcting section 160 performs the processing ofLoop H on each of the correction target candidate areas (Step F7 to StepF21).

In the processing of Loop H, the image correcting section 160 performsthe processing of Loop J on each of the pixels of the in-processcorrection target candidate area (Step F9 to Step F19). In theprocessing of Loop J, the processing of Loop K is performed on each ofthe non-base images (Step F11 to Step F17).

In the processing of Loop K, the image correcting section 160 makes adetermination as to whether the image number of the in-process non-baseimage is the same as the image number of the non-base minimum differenceimage stored in the non-base minimum difference map data 873 (Step F13).If the image number is not the same (Step F13, No), the processing ofStep F15 is skipped.

If the image number is the same (Step F13, Yes), the image correctingsection 160 performs the image correction processing on the in-processpixel so as to correct the pixel value of the base image by using thepixel value of the in-process non-base image (Step F15). The pixel valuemay be corrected by replacing (substituting) it with the pixel value ofthe in-process non-base image or with the weighted average of the baseimage and non-base images. The weights are not necessarily uniform overthe image. For example, they may be calculated with respect to eachpixel by using the similarity (difference in brightness, lightness orthe like of the pixels) between the base image and the non-base images.

In this embodiment, moving objects included in the foreground candidateareas can be erased by correcting the pixel values of the base image byusing the pixel values of the non-base images. This image correctionprocessing to erase moving objects is referred to as “replacementprocessing” in order to distinguish it from “filling processing”described below.

When the processing of Step F13 and Step F15 is performed on all of thenon-base images, the image correcting section 160 terminates theprocessing of Loop K (Step F17). Then, when the processing of Loop K isperformed on all of the pixels, the image correcting section 160terminates the processing of Loop J (Step F19). Then, upon completion ofthe above-described processing on all of the correction target candidateareas, the image correcting section 160 terminates the processing ofLoop H (Step F21).

Then, the image processing section 160 stores a data composed of thepixel values of the pixels stored in the output buffer 880 in the memorysection 800 as a correction image data 879 (Step F23). Then, the imagecorrecting section 160 terminates the image correction processing.

Back to the image processing, after the image correction processing isperformed, the display control section 170 controls the display section300 to display the corrected image that is composed of the pixel valuesstored in the output buffer 880 (Step B39).

Then, the processing section 100 makes a determination as to whether theuser has performed a correction mode terminating operation through theoperating section 200 (Step B41). If the correction mode terminatingoperation is not performed (Step B41, No), the processing section 100returns the processing to Step B25. If the correction mode terminatingoperation is performed (Step B41, Yes), the processing section 100terminates the image processing.

On the other hand, if the user selects an “image browsing mode” in StepB1 (Step B1, image browsing mode), the display controlling section 170controls the display section 300 to display a list of the image data 870stored in the memory section 800. Then, the processing section 100selects one piece of image data 870 from among the image data 870displayed in a list form according to a user operation on the operationsection 200 (Step B45).

Then, the processing section 100 terminates the image browsing mode andproceeds the processing to Step B5. The subsequent steps of theprocessing are the same. That is, the base image is set for theuser-selected image data 870, and the following image processing isperformed.

[2-4. Display Style of Foreground Candidate Area]

The information on the foreground candidate areas that are determined bythe foreground candidate area detecting section 140 and the foregroundcandidate area reliability determining section 150 is fed back to theuser through the display section 300. By suitably visualizing theinformation on the foreground candidate areas, the user can easily findthe candidate areas to be corrected.

The replacement candidate areas refer to candidates for areas where thepixel values are replaced with the pixel values of a differentphotographed image. They can be also referred to as replaceable areas.Further, in terms of replacing the pixel values of the areas with thepixel values of a different photographed image, they can also bereferred to as substitution candidate areas or substitutable areas. Inthis embodiment, the reliability is evaluated in three levels, “high”,“moderate” and “low”.

For example, the foreground candidate areas stored in the foregroundcandidate area data 877 are set as the replacement candidate areas. Thereplacement candidate areas are enlarged to the original size based onthe reduction ratio of the photographed images at the time of generatingthe analysis images, and the enlarged images are displayed at thecorresponding positions on the base images. By overlaying thereplacement candidate areas on the base image, the user can intuitivelyrecognize the areas to be corrected in the base image.

In this step, the contours of the foreground candidate areas stored inthe foreground candidate area data 877 may be displayed together withthe replacement candidate areas. The contours improve the visibility ofthe replacement candidate areas even if the color of the overlaidreplacement candidate areas is similar to the color of the base imagearound the areas. Further, as described below, the contours can be usedfor displaying additional information on the foreground candidate areas.

When the replacement candidate areas are overlaid on the base image,they may be painted with different colors according to the reliability.For example, the replacement candidate area with “high” reliability maybe displayed in “green” as being replaceable, and the replacementcandidate area with “moderate” reliability may be displayed in “yellow”as being replaceable with caution. Further, the replacement candidatearea with “low” reliability may be displayed in “red” as beingreplacement inadvisable. It is more preferred that the overlay isdisplayed with certain opacity (e.g. 50%) so that the user can easilyunderstand the replaceable items. This allows an intuitive andrecognizable indication of the reliability.

Instead of superimposing the replacement candidate areas painted withdifferent colors according to the reliability, for example, the contoursof the replacement candidate areas in different colors may besuperimposed on the base image. For example, the replacement candidatearea with “high” reliability may be displayed as a green contour, thereplacement candidate area with “moderate” reliability may be displayedas a yellow contour, and the replacement candidate area with “low”reliability may be displayed as a red contour.

In addition, a figure (e.g. a circle), a symbol, a character or the likemay be superimposed on the base image at the centers of gravity of theforeground candidate areas stored in the foreground candidate area data877 or of the replacement candidate areas. By means of thisconfiguration, the user can intuitively find the positions of theobjects to be corrected on the base image. Displaying the figure (e.g. acircle), symbol, character or the like centered at the center of gravityis particularly effective when it is difficult to visually recognize theobjects only by the contours, for example when a plurality of persons orobjects are overlapped with each other. For example, the position of thecenter of gravity may be obtained by approximating a foregroundcandidate area or a replacement candidate area with a rectangle andcalculating the center position of the rectangle, or by calculating theposition of a point within the area that has the least distancedeviation from a plurality of points on the outer circumference of aforeground candidate area or a replacement candidate area.

The above-described figure (e.g. a circle), symbol, character or thelike centered at the center of gravity may be displayed simultaneouslywith the contours on the same image. Alternatively, only theabove-described figure (e.g. a circle), symbol, character or the likecentered at the center of gravity may be displayed at first, andthereafter the contours may be displayed upon user-selection of thecorresponding figure, symbol, character or the like centered at thecenter of gravity. Further, as with the contours as described above,they may be displayed in different colors according to the reliability.

In this embodiment, all of the detected foreground candidate areas areset as the replacement candidate areas. However, the present disclosureis not limited thereto. For example, the foreground candidate areas withthe reliability determined as “low” may be excluded from the replacementcandidate areas so that the user cannot select them to be corrected. Inanother embodiment, the areas with “low” reliability may be displayed sothat the user can select candidates for the correction. In this case, adialog or the like that states that correction may not be performedproperly may be displayed at the time of selection in order to emphasizethat making corrections is at high risk of possibly failing appropriateimage correction.

[2-5. Experimental Result]

FIG. 18 (1) to FIG. 18 (3) illustrate an example of a replacementcandidate area display image and the corrected image.

FIG. 18 (1) illustrates the same photographed image as FIG. 10 (1). Whenthe foreground candidate area detection processing and the foregroundarea reliability determination processing are performed on the pluralityof photographed images including this photographed image, a foregroundcandidate area that corresponds to the male passerby is detected as areplacement candidate area R as illustrated in FIG. 18 (2). Thereliability of the replacement candidate area R is “high” in thisexample.

In this case, as illustrated in FIG. 18 (3), the replacement candidatearea R painted according to the reliability is overlaid on the baseimage (Step B23 of FIG. 13). Specifically, since the reliability of thereplacement candidate area R is “high”, the replacement candidate area Ris displayed in green. Further, by overlaying the replacement candidatearea R on the base image with certain opacity (e.g. 50%), the user caneasily find the effect of the correction by the replacement.

Then, when the user performs a tap gesture on the replacement candidatearea R in the displayed image to select the replacement candidate area Ras the correction target (Step B29 of FIG. 13), and further performs acorrection execution operation (Step B35, Yes), the image correctionprocessing is performed on the replacement candidate area R (Step B37 ofFIG. 13).

FIG. 18 (2) also illustrates an example of the contour and the center ofgravity of the replacement candidate area R. The contour and the centerof gravity may be colored according to the reliability. Further, thedevice may be configured such that the user can individually selectwhether to display them on the screen or hide them as necessary.Further, the contour and the center of gravity may be displayedsimilarly for the replacement candidate area R with predeterminedopacity as illustrated in FIG. 18 (3).

As a result of the image correction processing, the corrected image asillustrated in FIG. 18 (4) is obtained. In the corrected image, thepixel values in the area where the male passerby was present in thephotographed image of FIG. 18 (1) are corrected by using the pixelvalues of the non-base minimum difference image so that a moving subjectof the male passersby is erased.

FIG. 19 (1) to FIG. 19 (4) illustrate another example of the replacementcandidate area display image and the corrected image.

FIG. 19 (1) illustrates five photographic images that were taken atcertain time intervals. The photographic images were taken such that thebuilding was chosen as the photographic subject and the entrance of thebuilding was shown at the center. In the photographic images, manypassersby are shown in the foreground in addition to the building of thephotographic subject.

FIG. 19 (2) illustrates the non-base minimum difference image that isobtained by calculating the non-base minimum difference values from thephotographic images of FIG. 19 (1). In the non-base minimum differenceimage, the non-base minimum difference values are high in the areaslightly to the left of the center of the image. Since the area wascrowded and many passersby are therefore shown in the photographicimages, the calculated non-base minimum difference values become high inthe area.

FIG. 19 (3) illustrates the result of detecting the foreground candidateareas from the photographic images of FIG. 19 (1). In the image, sixforeground candidate areas R1 to R6 are detected (Step D11 of FIG. 15).As a result of performing the reliability determination processing onthe foreground candidate areas R1 to R6 to determine the reliability,the reliability of R1 is determined as “high” (replaceable) (Step E1 ofFIG. 16), the reliability of R2 and R3 is determined as “moderate”(replaceable with caution) (Step E15 of FIG. 16), and the reliability ofR6 is determined as “low” (irreplaceable) (Step E17 of FIG. 16). R4 andR5 are excluded from the object of the reliability determiningprocessing due to their sizes being too small (Step E19 of FIG. 16). Asa result, the foreground candidate areas R1 to R3 are determined as thereplacement candidate areas.

FIG. 19 (4) illustrates an example of the display image of thereplacement candidate areas. In the image, according to the reliabilityof the replacement candidate areas R1 to R3, R1 is displayed in green,and R2 and R3 are displayed in yellow (Step B23 of FIG. 13). R4 to R6are not displayed in the image since they are not determined as thereplacement candidate areas. In response to a user tap gesture on one ofthe replacement candidate areas R1 to R3 (Step B29 of FIG. 13, Yes), theimage is corrected in the area corresponding to the tapped replacementcandidate area (Step B37 of FIG. 13), and the corrected image isdisplayed (Step B39 of FIG. 13).

In the embodiment above, the image correction processing is performed inresponse to a user tap gesture on the replacement candidate areas.However, it is also advantageous to display a preview image in which anexpected corrected image with a predetermined opacity (e.g. 50%) issuperimposed on a photographed image before the image correctionprocessing is performed.

Specifically, back to FIG. 19 (1) to FIG. 19 (4), after the replacementcandidate area R is superimposed on the photographed image of FIG. 19(1), an expected corrected image is temporarily displayed in thereplacement candidate area R while the user is performing a touch (longtap) gesture on the replacement candidate area R. In this step, theexpected corrected image may be displayed with a predetermined opacityso that the photographed image of FIG. 19 (1) can be seen through theexpected corrected image.

Thereafter, after a certain time period has passed after the userreleases his/her finger from the screen, the expected corrected image iserased. However, if the user performs a double tap gesture on thereplacement candidate area R while the expected corrected image is stilldisplayed after the user releases his/her finger from the screen, theimage correction processing is performed on the replacement candidatearea R.

Together with the replacement candidate area R displayed with apredetermined opacity, a hatching, the contour or the center of gravitymay also be displayed in a color according to the reliability. By thisconfiguration, the user can easily understand the corrected image thatwill be obtained by the replacement before the actual correctionprocessing is performed.

In the processing from the detection of the foreground candidate areasto the determination of the replacement candidate areas, the device mayadditionally perform protection processing to forbid deleting aforeground candidate area that is detected in the area pre-specified bythe user or a forced deletion processing to always delete the foregroundcandidate area.

Specifically, back to FIG. 19 (1) to FIG. 19 (4), the user sets an areain advance, such as the rear-left of the building where R4 to R6 arepresent, as a protected area. Then, any foreground candidate areasdetected within the area may be protected as irreplaceable areasregardless of the size or the reliability, and may therefore remainundeleted. Alternatively, the user sets an area in advance such as therear-left of the building where R4 to R6 are present as a forceddeletion area. Then, any foreground candidate areas detected in the areaare forcibly deleted regardless of the size or the reliability.

The protected area or the forced deletion area may be specified by amethod known in the art such as the user surrounding a desired area onthe screen with a rectangle using his/her finger, a stylus or the like,or the function of an imaging software pre-installed in a personalcomputer or a mobile device. The addition of such functions allows acorrected image according to user preferences to be obtained moreeasily.

[2-6. Functions and Effects]

In the image processing device 3, the base image setting section 120selects the base image from among the plurality of photographed images.Then, the non-base minimum difference input image determining section117 determines the non-base minimum difference value, i.e. the lowestminimum value and the non-base image of the non-base minimum differencevalue with respect to each pixel from among the non-base images, so asto determine the non-base minimum difference image. Then, based on thedetermination result of the non-base minimum difference input imagedetermining section 117, the non-base minimum difference map generatingsection 119 generates the minimum difference map data 873 in which thenon-base minimum difference values and the non-base minimum differenceimages are stored in association with each other on a pixel basis. Thatis, the image processing device 3 also serves as the image processingdata generating device.

The foreground candidate detecting section 140 compares the basedifference value, which is obtained by calculating the backgrounddifference between the base image and the background image, with thenon-base minimum difference value, which is stored in the non-baseminimum difference map data 873, with respect to each pixel, so as todetect the foreground candidate areas, which are composed of pixels inwhich the base difference values differ from the non-base minimumdifference values by a certain extent or more. That is, the imageprocessing device 3 is provided with a foreground candidate areadetecting device for detecting the foreground candidate areas.

The foreground candidate area reliability determining section 150determines the reliability of the foreground candidate areas detected bythe foreground candidate area detecting section 140 being in theforeground. Then, based on the determination result of the reliability,the display controlling section 170 controls the display section 300 todisplay the reliability of each of the foreground candidate areas in auser-recognizable manner. By this configuration, the user canimmediately find the reliability of each of the foreground candidateareas being in the foreground. Therefore, the user can determine theforeground candidate areas to be corrected and give a command to thedevice to correct the determined foreground candidate areas in theimage.

The image correcting section 160 detects a tap gesture on the touchpanel 250. If the tap position is within the foreground candidate areas,it corrects the area of the base image corresponding to the tappedforeground candidate area. Then, the display controlling section 170controls the display section 300 to display the corrected image. Thatis, the image processing device 3 also serves as an image correctingdevice. By this configuration, the user can check the base image, and ifhe/she finds an unwanted object present, he/she can tap thecorresponding area to obtain the image in which the unwanted object isremoved.

The foreground candidate area reliability determining section 150excludes foreground candidate areas with a size less than apredetermined threshold from the processing object. If all foregroundcandidate areas are displayed, including such small areas, many smallforeground candidate areas caused by noise or the like are displayed,which can be annoying to the user. Further, even if such smallforeground candidate areas are corrected, such correction has littleadvantageous effect. By excluding such foreground candidate areas with asmall size from the processing object, these problems can be solved.

The foreground candidate reliability determining section 150 determinesthe reliability as “low” for a foreground candidate area that has a highnon-base minimum difference value, and makes the foreground candidatearea irreplaceable. The non-base minimum difference value tends to behigh in an area where an object is constantly moving. If imagecorrection is performed on such areas, a ghost may appear. However, bydetermining the reliability as “low” for a foreground candidate areawith a high non-base minimum difference value and making the areairreplaceable, the foreground candidate area is excluded from thereplacement candidate areas. Therefore, image correction that mayproduce a ghost can be avoided.

3. Third Embodiment

Next, in a third embodiment, the replacement processing, which is anexample of the image correction processing described in theabove-described embodiments, is combined with fill processing (alsoreferred to as paste processing), which is another example of the imagecorrection processing, so that moving objects other than thephotographic subject are erased.

As described in the second embodiment, the replacement processing can beperformed even on a foreground candidate area with low reliabilityaccording to a user designation or the like. However, such processingmay result in obtaining an improper corrected image. To avoid this, thefollowing filling processing, which is an alternative to replacementprocessing for image correction processing, is effective for suchforeground candidate areas with low reliability.

Specifically, a target area to be filled (hereinafter referred to as a“fill target area”) is selected from among the foreground candidateareas of the base image. Then, a sub-area that is similar to a sub-areain the fill target area is searched among (1) the base image itself, (2)one of the plurality of input images, or (3) an image saved in the otherdatabase, and the pixel values of the pixels in the sub-area in the filltarget area in the base image are replaced with the pixel values of thesimilar sub-area that is specified by the search. That is, as the imagecorrection processing, image data similar to the image data of theforeground candidate area is searched for among the images forcorrection, and the searched data is pasted in the foreground candidatearea of the image to be corrected.

In this step, the filling processing is performed sequentially in aninward direction from the outer edge to the inside of the fill targetarea. This can prevent the interface between the filled area and thesurrounding area from being visualized. To search similar sub-areas, themeasure of similarity may be calculated by, for example, squaring thedifference in pixel values (brightness or lightness).

The method of the image correction processing may be switched accordingto the reliability of the foreground candidate areas so as to correctthe areas corresponding to the foreground candidate areas, for exampleby performing the replacement processing on the foreground candidateareas with “high” or “moderate” reliability and performing the fillprocessing on the foreground candidate areas with “low” reliability.

Alternatively, the device may be configured such that the user canselect the image correction processing between the replacementprocessing and the fill processing, and the device performs theuser-selected image correction processing so as to correct the image.

4. Variation

The embodiments of the present disclosure are not limited to theabove-described embodiments, and it should be understood that changesand modifications may be suitably made without departing from the scopeof the present disclosure. Hereinafter, some variations will bedescribed. In the following description, the same reference signs aredenoted to the same components of the above-described embodiments, andthe description thereof is omitted.

[4-1. Electronic Device]

The image processing device 3 can be installed in electronic devicessuch as smartphones, cameras, digital cameras, tablets, PDAs andpersonal computers. A variety of electronic devices that include animaging section may be provided with the image processing device 3 ofthe present disclosure.

While the user performs a tap gesture on the replacement candidate areasdisplayed on the display section 300 of the image processing device 3,the image corrected by the image processing device 3 may be displayed onanother display section of the other information processing device.Specifically, if the image processing device 3 is a smartphone, forexample, the user establishes a communication connection between thesmartphone in his/her hand with a personal computer. Then, the userperforms a tap gesture on the replacement candidate areas displayed onthe display of the smartphone. In response to detecting the user tapgesture, the processing section of the smartphone performs the imagecorrection processing and transmits the corrected image to the personalcomputer. The personal computer displays the corrected image receivedfrom the smartphone on a display, and the user checks the correctedimage on the display of the personal computer.

Further, the set of image data may be transmitted from the electronicdevice such as a smartphone to the personal computer so that thephotographed images are input from the electronic device such as thesmartphone to the personal computer. The personal computer then performsthe same image correction as the above-described embodiment by using theinput images so as to generate the corrected image and to display it onits display. In this case, the personal computer serves as the imageprocessing device 3 of the present disclosure.

[4-2. Correction Target Area]

In the above-described embodiments, the image processing device 3detects the foreground candidate areas that are candidates of theforeground in the base image by using the plurality of input images andthe image processing data in order to detect the correction targetareas. However, the method of detecting the correction target areas isnot limited thereto.

For example, characteristic point detection processing may be performedon each of the plurality of input images to detect characteristicpoints, and the moving object areas, which are an area where an objectis moving, may be detected as correction target areas based on therelationship between the detected characteristic points. The relation ofthe characteristic points can be determined by a method known in the artsuch as a block matching method and a gradient method.

Specifically, areas where the amount of change (displacement) exceeds apredetermined threshold at the position of a characteristic point in aplurality of input images may be detected as moving object areas. Inthis case, the “motion degree” may be determined for each of thedetected moving object areas as the reliability based on the degree ofmotion of the moving object areas, and the suitability of correction maybe determined for each of the motion object areas. For example, thedegree of motion may be graded into “high”, “moderate” and “low”according to the displacement of the characteristic points, and themoving object areas with “high” or “moderate” degree of motion may bedetermined as the moving object areas to be corrected. Then, thedetermined moving object areas may be set as the correction targetareas, and the above-described replacement processing may be performedto correct the image.

The moving object areas with “low” degree of motion may also be includedin the correction target areas, and the fill processing may be performedon these moving object areas to correct the image.

In addition to such moving object areas, the pixel values of an area maychange between the plurality of input images due to a change of lightduring the photographing or the like even though the same object remainsin the area. To cope with the problem, light affected areas where thepixel values changes by a certain amount or more between the pluralityof input images may be detected as the correction target areas.

Specifically, an area where the amount of change in pixel value (e.g.the average of the pixel values in the area) exceeds a predeterminedthreshold is detected as a light affected area. Then, the “light effectdegree” is determined, which is the reliability based on the degree ofthe effect of light in the light affected area, and it is determinedwhether to correct the light affected area based on the determined lighteffect degree. For example, the light effect degree is graded into“high”, “moderate” or “low” according to the amount of change in pixelvalue, and a light affected area with “high” or “moderate” light effectdegree” is determined as a light affected area to be corrected. Then,the determined light affected area is set as the correction target area,and the image correction is performed by the above-described replacementprocessing.

The light affected areas with “low” light effect degree may also beincluded in the correction target areas, and the fill processing may beperformed on these light affected areas to correct the image.

The above-described moving object area and light affected area areexamples of the appearance changing area where the appearance of asubject changes between the plurality of input images.

[4-3. Processing Pixel Unit]

In the above-described embodiments, the processing pixel unitcorresponds to a pixel (single pixel), and the series of processing forgenerating the image processing data is performed on a pixel basis.However, the processing pixel unit may correspond to an area composed ofa plurality of pixels rather than corresponding to a single pixel. Forexample, the pixels of an image may be divided in a lattice pattern, andthe same processing as the above-described embodiments may be performedon each divided tile as a processing pixel unit to generate the imageprocessing data.

In this case, for example, the processing pixel unit may correspond to asquare tile such as 10×10 pixels and 20×20 pixels or a rectangular areasuch as 10×20 pixels and 20×10 pixels.

The size of the processing pixel unit may be suitably changed based onthe size of the input images. For example, the size of the processingpixel unit may be selected within the range of 1% to 10% of the size ofthe input images, and the processing may be performed with respect toeach processing pixel unit.

[4-4. Image Processing Data]

In the above-described embodiments, the input image in which thedifference value between the input images and the reference image is atits lowest is determined as the input image that satisfies the specificcondition, and the image processing data is generated in which theidentification information of the minimum difference input images andthe respective minimum difference values are associated with each other.Instead, the generated image processing data may contain only theidentification information of the minimum difference input images on aprocessing pixel unit basis rather than containing the minimumdifference values. Conversely, the generated image processing data maycontain only the minimum difference values on a processing pixel unitbasis rather than containing the identification information of theminimum difference input images.

The same applies when a different condition is used as the specificcondition. For example, when an input image in which the differencevalue is at its highest is determined as the input image that satisfiesthe specific condition, the generated image processing data may containonly the identification information of the maximum difference inputimages on a processing pixel unit basis or only the maximum differencevalues on a processing pixel unit basis.

[4-5. Automatic Determination of Correction Target Foreground CandidateArea]

In the image processing device 3 of the above-described embodiment, theforeground candidate areas (or the replacement candidate areas) to becorrected are determined according to a user operation. Instead, theimage processing device 3 may be configured to automatically determinethe areas to be corrected and to perform the image correction.

Further, a preferential correction area may be specified according to auser finger gesture on the touch panel 250 or the like. In this case,for example, the display section 300 is controlled to display aplurality of face areas detected by face detection processing so thatthe user can specify a preferential correction area from among thedisplayed plurality of face areas.

Alternatively, a data of plural patterns of the preferential correctionarea (hereinafter referred to as “preferential correction areapatterns”) may be stored in the memory section 800, and the displaysection 300 may be controlled to display the preferential correctionarea patterns so that the user can select a pattern to be used as thepreferential correction area from among them.

FIG. 20 (1) to FIG. 20 (3) illustrate examples of the preferentialcorrection area patterns. In the figures, the hatched areas representcandidates of the preferential correction area.

FIG. 20 (1) illustrates a pattern in which a vertical band preferentialcorrection area is located in the center part of an image. FIG. 20 (2)illustrates a pattern in which a frame preferential correction area islocated at the rim part of an image. FIG. 20 (3) illustrates a patternin which a triangular preferential correction area extends from theupper part to the lower part of an image.

These preferential correction area patterns are merely examples, andother various preferential correction area patterns may also bepre-defined.

After the preferential correction areas are determined (confirmed) asdescribed above, the display control section 170 controls the displaysection 300 to display the preferential correction areas in a mannerdistinguishable from the foreground candidate areas. Specifically, forexample, the preferential correction areas are displayed in a differentcolor from the foreground candidate areas (or the replacement candidateareas). In this case, the area itself or the contour of the area may becolored. By this configuration, the user can immediately distinguish theareas that the device automatically determines for preferentialcorrection from the other foreground candidate areas.

The image correcting section 160 performs the image correction on thepreferential correction areas determined as described above in the samemanner as the above-described embodiments. Then, the display controllingsection 170 controls the display section 300 to display the imagecorrected by the image correcting section 160.

Alternatively, the foreground candidate areas to be corrected(correction target foreground candidate areas) may be determined basedon the reliability of the foreground candidate areas. Specifically, forexample, the foreground candidate areas with particularly highreliability may be determined as the correction target foregroundcandidate areas. For example, the foreground candidate areas with thereliability determined as “high” in the above-described embodiments maybe determined as the correction target foreground candidate areas.

After the reliability is determined, the image correcting section 160may automatically perform the image correction on the foregroundcandidate areas with particularly high reliability in the same manner asthe above-described embodiments. Subsequently, it may use the correctedimage as a default image to further perform the automatic imagecorrection and the user operation-based determination and correction ofthe foreground candidate areas of the above-described embodiments. Byautomatically correcting the foreground candidate areas withparticularly high reliability in advance in this way, the subsequentprocessing can be performed more rapidly and more easily.

[4-6. Automatic Setting of Base Image]

In the above-described embodiments, to select the base image from amongthe plurality of photographed images, the user is prompted to select animage from among the plurality of photographed images, or thephotographed image that is input first or last is set as the base image.Instead, an image that is selected according to a predeterminedcriterion may be automatically set as the base image. For example, in agroup photo in which a plurality of persons are present as thephotographic subjects, the front level or the smile level of the personsof the photographic subjects is detected by a method known in the art,and an input image that scores a predetermined standard level or more inthe front level or the smile level may be selected as the base image.

Furthermore, the absence of image blur due to a shaking hand or theabsence of backlight condition may be detected by a method known in theart and used as an additional condition of the base image.

[4-7. Other Embodiments of Replacement Candidate Area Determination]

In the above-described embodiments, the image processing device 3automatically determines the reliability of the replacement candidateareas based on the size of the foreground candidate areas and the resultof comparing the non-base minimum difference values with the threshold,and sets the areas with “low” reliability as irreplaceable areas.Further, in order to set the areas with the reliability determined as“low” as replaceable areas, a user operation of designating the areas orthe like is required. However, when the size of the areas with “low”reliability is greater than a predetermined value, the areas may beautomatically set as replaceable areas. Specifically, foregroundcandidate areas with the reliability determined as “low” combine witheach other to form a group of areas with a predetermined number or moreof pixels or a predetermined size or more, and thereby the area may beautomatically set as the replacement candidate area.

Specifically, the pixels of the correction target image may be dividedinto processing pixel units having a predetermined shape (e.g.rectangular shape), the reliability of being in the foreground may bedetermined for each of the processing pixel units. In this case, withrespect to each processing pixel unit, the degree of motion of an objectis determined based on the amount of displacement of the characteristicpoints, in which a processing pixel unit with higher motion degree isdetermined to have higher reliability of being in the foreground (“high”reliability) and a processing pixel unit with lower motion degree isdetermined to have lower reliability of being in the foreground (“low”reliability). Then, for example, an area composed of a group ofcontinuing processing pixel units with “high” reliability is set as thereplacement candidate area. Further, when such areas satisfy thecondition that the number of pixels or the size of such areas is equalto or greater than a predetermined value, such areas may be determinedto remain the replacement candidate areas. When the condition is notsatisfied, such areas may be excluded from the replacement candidateareas.

Such areas may not necessarily combine with each other. Instead, thenumber of pixels or the area of the foreground candidate areas per unitarea of the displayed image may be detected, and an area with apredetermined pixel density or more or a predetermined area density ormore may be set as the replacement candidate area.

By this configuration, even if there are a relatively large number ofareas with “low” reliability, the image processing device 3 canautomatically and effectively perform the image correction.

[4-8. Refresh of Input Image]

In the above-described image processing device 3, the indication of thereliability of the foreground candidate areas may be displayed on thephotographed image in real-time.

Specifically, the processing section 100 stores the most recentpredetermined frames of input photographed images in the memory section800. That is, the memory section 800 serves as an image storing means.In this case, for example, a ring buffer that can store thepredetermined number of images is constructed in the memory section 800so that the most recent predetermined frames of the photographed imagesare always stored in the ring buffer.

Further, the base image setting section 120 sets the most recentphotographed image stored in the memory section 800 as the base image.Then, by using the latest predetermined number of photographed imagesstored in the memory section 800 as the base image and the non-baseimages, the processing section 100 performs the non-base minimumdifference map data generation processing, the foreground candidate areadetermination processing and the foreground candidate area reliabilitydetermination processing in the same manner as the above-describedembodiments.

The display controlling section 170 controls the display section 300 todisplay the base image together with the replacement candidate areassuperimposed thereon or the contours of the replacement candidate areasdrawn thereon. In superimposing the replacement candidate areas ordrawing the contours of the replacement candidate areas, the displaycontrolling section 170 controls the display section 300 to indicate thereplacement reliability of the replacement candidate areas in auser-recognizable manner. For example, the replacement candidate areasare superimposed on the base image by using different colors accordingto the replacement reliability, or the contour of the replacementcandidate areas are drawn on the base image with different colorsaccording to the replacement reliability.

The above-described processing is constantly performed at each time orat a certain interval a new photographed image is input. That is, theprocessing section 100 constantly refreshes the base image in responseto an input of a new photographed image and re-calculates thereplacement candidate areas and the replacement reliability. Then, basedon the result of the re-calculation, the display controlling section 170controls the display section 300 to constantly refresh the displayedreplacement candidate areas and the indication of the replacementreliability.

In this way, every time a new image is input, the oldest image isdiscarded, and re-calculation is performed. Accordingly, the user canfind the replacement candidate areas in real-time. Further, in an areawhere it is difficult to perform the replacement, the user can waituntil the replacement candidate areas become replaceable before givingan image correction command at a suitable timing.

Further, the user may set an area where it is difficult to perform thereplacement (hereinafter referred to as a “replacement difficult area”)beforehand, and the processing section 100 may automatically correct thearea when all of the foreground candidate areas that at least partlyoverlap with the area become replaceable as a result of an input of anew photographed image.

In this case, to allow the user to set the replacement difficult area, amethod similar to the method of setting the preferential correction areadescribed in “3-4. Automatic Setting of Correction Target ForegroundCandidate Area” may be used. That is, the user may set an area in theimage where it is difficult to perform the replacement by a tap gestureon the touch panel 250 or the like. Alternatively, a data of a pluralityof patterns of the replacement difficult area (replacement difficultarea patterns) may be pre-stored in the memory section 300, and the usermay select a pattern of the replacement difficult area from among thestored patterns.

The image processing device 3 may be configured to be able to performboth of the above-described automatic correction processing of thecorrection target areas and the manual correction processing based on auser manual operation. For example, the image correction mode may startafter the automatic correction is performed on the automaticallydetermined correction target areas, and the user can manually set thenecessity of correction for the other areas where the automaticcorrection is not performed. In this case, the display section 300 maybe controlled to display an image that is corrected by the automaticcorrection, and the user may be prompted to set the necessity ofcorrection for the other areas.

The background image may be kept in storage in the memory section 800rather than being discarded. Further, the device may be configured notto erase the oldest photographed image and to accumulate newly inputphotographed images.

[4-9. Restore Processing of Corrected Image]

After the corrected image on which the image correction is performed isdisplayed, a restore processing may be performed according to a useroperation to return the corrected areas to the original, and the displaysection 300 may be controlled to display the resultant image. The userwho browses the corrected image sometimes is not satisfied with thecorrection and wants to put some of the corrected areas in the image,which correspond to the replacement candidate areas, back to theoriginal. To cope with this, among the replacement candidate areas witha correction flag that is ON, it is preferred that the image data in thereplacement candidate areas that are specified by a user restoreoperation is replaced with the image data of the original base image,and the display control section 300 is controlled to display theresultant image.

[4-10. Recording Medium]

In the above-described embodiments, the programs and data relating tothe generation of the image processing data and the image processing arestored in the memory section 20 of the image processing data generatingdevice 1 and the memory section 800 of the image processing device 3,and the processing section reads out and executes these programs so asto perform the image processing of the above-described embodiments. Inthese cases, the memory section of each device may include a recordingmedium such as a memory card (SD card), a COMPACT FLASH (registeredtrademark), a memory stick, a USB memory, a CD-RW (optical disk) and anMO (magnetooptic disk) instead of an internal storage such as an ROM, anEEPROM, a flash memory, a hard disk and an RAM, and the above-describedprograms and data may be stored in the recording medium.

FIG. 21 illustrates an example of the recording medium in these cases.

The image processing device 3 includes a card slot 710 to which a memorycard 7 is inserted and a card reader/writer (R/W) 720 to read/writeinformation from/to the memory card 7 inserted in the card slot 710. Thecard reader/writer 720 reads/writes the programs and data in/from thememory section 800 and the information stored in the memory card 7according to a control of the processing section 100. Further, theprograms and data stored in the memory card 7 is structured such that anexternal device (e.g. a personal computer) other than the imageprocessing device 3 can also read them and perform the image processingof the above-described embodiments therein.

The invention claimed is:
 1. An image processing device, comprising: adisplay unit; a detecting unit configured to detect a correctioncandidate area in a correction target image based on a plurality ofinput images; a reliability determining unit configured to determine acorrection reliability of the correction candidate area; a displaycontrolling unit configured to control the display unit to display thecorrection target image, the correction candidate area and thedetermined correction reliability; and an image correcting unitconfigured to correct the correction candidate area based on a useroperation, wherein the reliability determining unit determines thecorrection reliability in a one-to-one correspondence with thecorrection candidate area, and the display controlling unit controls thedisplay unit to display whether to correct the correction candidate areain a user-recognizable manner on a basis of the determined correctionreliability.
 2. The image processing device according to claim 1,wherein the display controlling unit perform one of the followingdisplay control: the display control to control the display unit todisplay the correction candidate area on the correction target image, ina manner that reflects the determined correction reliability to indicatethat correcting the correction candidate area is more appropriate as thecorrection reliability is higher, and the display control to display animage in which at least one of a contour of the correction candidatearea or a center of gravity of the correction candidate area on thecorrection target image, in a manner that reflects the determinedcorrection reliability to indicate that correcting the correctioncandidate area is more appropriate as the correction reliability ishigher.
 3. The image processing device according to claim 2, wherein thedisplay controlling unit further perform the display control so that acorrection target is contained in the correction candidate area in theuser-recognizable manner.
 4. The image processing device according toclaim 1, wherein the detecting unit detects a plurality of correctioncandidate areas in the correction target image, the reliabilitydetermining unit determines the correction reliability of each of thecorrection candidate areas in the one-to-one correspondence with each ofthe plurality of correction candidate areas, and the display controllingunit controls the display unit to display the correction target image,the plurality of correction candidate areas and the determinedrespective correction reliability, the display controlling unit controlsthe display unit to display whether to correct each of the correctioncandidate areas in the user-recognizable manner on the basis of thedetermined correction reliability for each of the plurality ofcorrection candidate areas.
 5. The image processing device according toclaim 4, wherein the display controlling unit controls the display unitto display the plurality of correction candidate areas on the correctiontarget image, in a manner that reflects the determined respectivecorrection reliability for each of the plurality of correction candidateareas to indicate that correcting the correction candidate area is moreappropriate as the correction reliability is higher.
 6. The imageprocessing device according to claim 4, wherein the image correctingunit sets a correction candidate area from the plurality of correctioncandidate areas specified by a first user operation and to correct theset correction candidate area based on a second user operation.
 7. Theimage processing device according to claim 6, further comprising: anotifying unit configured to notify a predetermined caution when thedetermined correction reliability of the correction candidate areaspecified by the first user operation does not satisfy a predeterminedcondition.
 8. The image processing device according to claim 1, whereinthe display controlling unit controls the display unit to inhibitdisplaying of the correction candidate area when the determinedcorrection reliability of the correction candidate area does not satisfya predetermined condition.
 9. The image processing device according toclaim 1, wherein the image correcting unit inhibits correcting of thecorrection candidate area when the determined correction reliability ofthe correction candidate area does not satisfy a predeterminedcondition, regardless of the user operation.
 10. The image processingdevice according to claim 1, wherein the image correcting unit correctsthe correction candidate area of the correction target image by usingimage correction processing that is selected based on the user operationor the determined correction reliability of the correction candidatearea.
 11. The image processing device according to claim 10, wherein theimage correcting unit performs the image correction processing thatreplaces an image data of the correction candidate area of thecorrection target image with an image data of an area corresponding tothe correction candidate area of a correction image that is at least oneof the plurality of input images.
 12. The image processing deviceaccording to claim 10, wherein the image correcting unit performs theimage correction processing that extracts an image data that satisfies apredetermined relationship with image data of the correction candidatearea from a correction image that is at least one of the plurality ofinput images, and pastes the extracted image data to the correctioncandidate area of the correction target image.
 13. The image processingdevice according to claim 1, wherein the display controlling unitcontrols the display unit to display an expected corrected image of thecorrection candidate area on the correction target image, in a mannerthat reflects the determined correction reliability.
 14. The imageprocessing device according to claim 13, wherein the image correctingunit corrects the correction candidate area based on the user operationwhile the expected corrected image is displayed.
 15. The imageprocessing device according to claim 13, wherein the display controllingunit controls the display unit to display the expected corrected imageon the correction target image with a predetermined opacity.
 16. Theimage processing device according to claim 1, further comprising: apreferential correction area determining unit, configured to determine apreferential correction area specified by the user operation, thepreferential correction area being an area in the correction targetimage that is preferentially corrected, wherein the display controllingunit controls the display unit to display the preferential correctionarea on the correction target in a user-recognizable manner, and theimage correcting unit preferentially corrects the preferentialcorrection area of the correction target image.
 17. The image processingdevice according to claim 1, wherein the image correcting unit inhibitscorrecting of a correction candidate area in an inhibited correctionarea specified by the user operation, regardless of the determinedcorrection reliability of the correction candidate area.
 18. The imageprocessing device according to claim 1, wherein the image correctingunit forcibly corrects a correction candidate area in a forcedcorrection area specified by the user operation, regardless of thedetermined correction reliability of the correction candidate area. 19.An image processing method, comprising: detecting a correction candidatearea in a correction target image based on a plurality of input images;determining a correction reliability of the correction candidate area;displaying the correction target image, the correction candidate areaand the determined correction reliability; and correcting the correctioncandidate area based on a user operation, wherein the correctionreliability is determined in a one-to-one correspondence with thecorrection candidate area, and whether to correct the correctioncandidate area in a user-recognizable manner is displayed on a basis ofthe determined correction reliability.
 20. A non-transitory computerreadable recording medium storing a program to make a computer executethe steps of: a detecting step of detecting a correction candidate areain a correction target image based on a plurality of input images; areliability determining step of determining a correction reliability ofthe correction candidate area; a display step of displaying thecorrection target image, the correction candidate area and thedetermined correction reliability; and an image correcting step ofcorrecting the correction candidate area based on a user operation,wherein the reliability determining step determines the correctionreliability in a one-to-one correspondence with the correction candidatearea, and the display step displays whether to correct the correctioncandidate area in a user-recognizable manner on a basis of thedetermined correction reliability.